{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FSRS4Anki Optimizer mini-batch_pow-forget-curve"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-spaced-repetition/fsrs4anki/blob/main/archive/experiment/mini-batch_pow-forget-curve.ipynb)\n",
    "\n",
    "↑ Click the above button to open the optimizer on Google Colab.\n",
    "\n",
    "> If you can't see the button and are located in the Chinese Mainland, please use a proxy or VPN."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upload your **Anki Deck Package (.apkg)** file or **Anki Collection Package (.colpkg)** file on the `Left sidebar -> Files`, drag and drop your file in the current directory (not the `sample_data` directory). \n",
    "\n",
    "No need to include media. Need to include scheduling information. \n",
    "\n",
    "> If you use the latest version of Anki, please check the box `Support older Anki versions (slower/larger files)` when you export.\n",
    "\n",
    "You can export it via `File -> Export...` or `Ctrl + E` in the main window of Anki.\n",
    "\n",
    "Then replace the `filename` with yours in the next code cell. And set the `timezone` and `next_day_starts_at` which can be found in your preferences of Anki.\n",
    "\n",
    "After that, just run all (`Runtime -> Run all` or `Ctrl + F9`) and wait for minutes. You can see the optimal parameters in section **2.3 Result**. Copy them, replace the parameters in `fsrs4anki_scheduler.js`, and paste them into the custom scheduling of your deck options (require Anki version >= 2.1.55).\n",
    "\n",
    "**NOTE**: The default output is generated from my review logs. If you find the output is the same as mine, maybe your notebook hasn't run there.\n",
    "\n",
    "**Contribute to SRS Research**: If you want to share your data with me, please fill this form: https://forms.gle/KaojsBbhMCytaA7h8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here are some settings that you need to replace before running this optimizer.\n",
    "\n",
    "filename = \"../collection-2022-09-18@13-21-58.colpkg\"\n",
    "# If you upload deck file, replace it with your deck filename. E.g., ALL__Learning.apkg\n",
    "# If you upload collection file, replace it with your colpgk filename. E.g., collection-2022-09-18@13-21-58.colpkg\n",
    "\n",
    "# Replace it with your timezone. I'm in China, so I use Asia/Shanghai.\n",
    "# You can find your timezone here: https://gist.github.com/heyalexej/8bf688fd67d7199be4a1682b3eec7568\n",
    "timezone = 'Asia/Shanghai'\n",
    "\n",
    "# Replace it with your Anki's setting in Preferences -> Scheduling.\n",
    "next_day_starts_at = 4\n",
    "\n",
    "# Replace it if you don't want the optimizer to use the review logs before a specific date.\n",
    "revlog_start_date = \"2006-10-05\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Build dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Extract Anki collection & deck file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extract successfully!\n"
     ]
    }
   ],
   "source": [
    "import zipfile\n",
    "import sqlite3\n",
    "import time\n",
    "from tqdm import notebook\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from datetime import timedelta, datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import sys\n",
    "import torch\n",
    "from torch import nn\n",
    "from sklearn.utils import shuffle\n",
    "# Extract the collection file or deck file to get the .anki21 database.\n",
    "with zipfile.ZipFile(f'./{filename}', 'r') as zip_ref:\n",
    "    zip_ref.extractall('./')\n",
    "    print(\"Extract successfully!\")\n",
    "\n",
    "\n",
    "notebook.tqdm.pandas()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Create time-series feature & analysis\n",
    "\n",
    "The following code cell will extract the review logs from your Anki collection and preprocess them to a trainset which is saved in [./revlog_history.tsv](./revlog_history.tsv).\n",
    "\n",
    "The time-series features are important in optimizing the model's parameters. For more detail, please see my paper: https://www.maimemo.com/paper/\n",
    "\n",
    "Then it will generate a concise analysis for your review logs. \n",
    "\n",
    "- The `r_history` is the history of ratings on each review. `3,3,3,1` means that you press `Good, Good, Good, Again`. It only contains the first rating for each card on the review date, i.e., when you press `Again` in review and  `Good` in relearning steps 10min later, only `Again` will be recorded.\n",
    "- The `avg_interval` is the actual average interval after you rate your cards as the `r_history`. It could be longer than the interval given by Anki's built-in scheduler because you reviewed some overdue cards.\n",
    "- The `avg_retention` is the average retention after you press as the `r_history`. `Again` counts as failed recall, and `Hard, Good and Easy` count as successful recall. Retention is the percentage of your successful recall.\n",
    "- The `stability` is the estimated memory state variable, which is an approximate interval that leads to 90% retention.\n",
    "- The `factor` is `stability / previous stability`.\n",
    "- The `group_cnt` is the number of review logs that have the same `r_history`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "revlog.csv saved.\n"
     ]
    },
    {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainset saved.\n"
     ]
    },
    {
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retention calculated.\n"
     ]
    },
    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stability calculated.\n"
     ]
    },
    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "      r_history  avg_interval  avg_retention  stability  factor  group_cnt\n",
      "              1           1.7          0.765        1.0     inf       7997\n",
      "            1,3           3.9          0.876        4.2    4.20       4176\n",
      "          1,3,3           8.6          0.883        9.2    2.19       2711\n",
      "        1,3,3,3          18.2          0.858       14.0    1.52       1616\n",
      "      1,3,3,3,3          37.5          0.835       23.2    1.66        822\n",
      "    1,3,3,3,3,3          78.8          0.850       35.6    1.53        384\n",
      "  1,3,3,3,3,3,3         122.3          0.903       39.3    1.10        171\n",
      "              2           1.0          0.901        1.1     inf        240\n",
      "            2,3           3.5          0.946        8.3    7.55        201\n",
      "          2,3,3          11.1          0.890        7.1    0.86        160\n",
      "              3           1.5          0.962        5.4     inf       9134\n",
      "            3,3           3.9          0.966       15.2    2.81       6589\n",
      "          3,3,3           9.0          0.960       23.7    1.56       5162\n",
      "        3,3,3,3          19.4          0.942       44.2    1.86       3519\n",
      "      3,3,3,3,3          39.1          0.926       54.5    1.23       1922\n",
      "    3,3,3,3,3,3          76.6          0.930      106.8    1.96       1074\n",
      "  3,3,3,3,3,3,3         118.7          0.949      155.3    1.45        480\n",
      "3,3,3,3,3,3,3,3         131.1          0.970      617.2    3.97        100\n",
      "              4           3.8          0.966       12.1     inf      11599\n",
      "            4,3           8.1          0.975       38.9    3.21       7517\n",
      "          4,3,3          18.0          0.963       56.8    1.46       5303\n",
      "        4,3,3,3          33.3          0.952       84.3    1.48       3012\n",
      "      4,3,3,3,3          48.3          0.953      128.3    1.52       1353\n",
      "    4,3,3,3,3,3          67.3          0.958      112.9    0.88        496\n",
      "  4,3,3,3,3,3,3          77.6          0.978      113.2    1.00        244\n",
      "4,3,3,3,3,3,3,3         112.9          0.984      177.8    1.57        168\n",
      "Analysis saved!\n"
     ]
    }
   ],
   "source": [
    "if os.path.isfile(\"collection.anki21b\"):\n",
    "    os.remove(\"collection.anki21b\")\n",
    "    raise Exception(\n",
    "        \"Please export the file with `support older Anki versions` if you use the latest version of Anki.\")\n",
    "elif os.path.isfile(\"collection.anki21\"):\n",
    "    con = sqlite3.connect(\"collection.anki21\")\n",
    "elif os.path.isfile(\"collection.anki2\"):\n",
    "    con = sqlite3.connect(\"collection.anki2\")\n",
    "else:\n",
    "    raise Exception(\"Collection not exist!\")\n",
    "cur = con.cursor()\n",
    "res = cur.execute(\"SELECT * FROM revlog\")\n",
    "revlog = res.fetchall()\n",
    "\n",
    "df = pd.DataFrame(revlog)\n",
    "df.columns = ['id', 'cid', 'usn', 'r', 'ivl',\n",
    "              'last_lvl', 'factor', 'time', 'type']\n",
    "df = df[(df['cid'] <= time.time() * 1000) &\n",
    "        (df['id'] <= time.time() * 1000) &\n",
    "        (df['r'] > 0)].copy()\n",
    "df['create_date'] = pd.to_datetime(df['cid'] // 1000, unit='s')\n",
    "df['create_date'] = df['create_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df['review_date'] = pd.to_datetime(df['id'] // 1000, unit='s')\n",
    "df['review_date'] = df['review_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df.drop(df[df['review_date'].dt.year < 2006].index, inplace=True)\n",
    "df.sort_values(by=['cid', 'id'], inplace=True, ignore_index=True)\n",
    "type_sequence = np.array(df['type'])\n",
    "time_sequence = np.array(df['time'])\n",
    "df.to_csv(\"revlog.csv\", index=False)\n",
    "print(\"revlog.csv saved.\")\n",
    "df = df[df['type'] != 3].copy()\n",
    "df['real_days'] = df['review_date'] - timedelta(hours=next_day_starts_at)\n",
    "df['real_days'] = pd.DatetimeIndex(df['real_days'].dt.floor('D', ambiguous='infer', nonexistent='shift_forward')).to_julian_date()\n",
    "df.drop_duplicates(['cid', 'real_days'], keep='first', inplace=True)\n",
    "df['delta_t'] = df.real_days.diff()\n",
    "df.dropna(inplace=True)\n",
    "df['delta_t'] = df['delta_t'].astype(dtype=int)\n",
    "df['i'] = 1\n",
    "df['r_history'] = \"\"\n",
    "df['t_history'] = \"\"\n",
    "col_idx = {key: i for i, key in enumerate(df.columns)}\n",
    "\n",
    "\n",
    "# code from https://github.com/L-M-Sherlock/anki_revlog_analysis/blob/main/revlog_analysis.py\n",
    "def get_feature(x):\n",
    "    last_kind = None\n",
    "    for idx, log in enumerate(x.itertuples()):\n",
    "        if last_kind is not None and last_kind in (1, 2) and log.type == 0:\n",
    "            return x.iloc[:idx]\n",
    "        last_kind = log.type\n",
    "        if idx == 0:\n",
    "            if log.type != 0:\n",
    "                return x.iloc[:idx]\n",
    "            x.iloc[idx, col_idx['delta_t']] = 0\n",
    "        if idx == x.shape[0] - 1:\n",
    "            break\n",
    "        x.iloc[idx + 1, col_idx['i']] = x.iloc[idx, col_idx['i']] + 1\n",
    "        x.iloc[idx + 1, col_idx['t_history']] = f\"{x.iloc[idx, col_idx['t_history']]},{x.iloc[idx, col_idx['delta_t']]}\"\n",
    "        x.iloc[idx + 1, col_idx['r_history']] = f\"{x.iloc[idx, col_idx['r_history']]},{x.iloc[idx, col_idx['r']]}\"\n",
    "    return x\n",
    "\n",
    "df = df.groupby('cid', as_index=False, group_keys=False).progress_apply(get_feature)\n",
    "df = df[df['id'] >= time.mktime(datetime.strptime(revlog_start_date, \"%Y-%m-%d\").timetuple()) * 1000]\n",
    "df[\"t_history\"] = df[\"t_history\"].map(lambda x: x[1:] if len(x) > 1 else x)\n",
    "df[\"r_history\"] = df[\"r_history\"].map(lambda x: x[1:] if len(x) > 1 else x)\n",
    "df.to_csv('revlog_history.tsv', sep=\"\\t\", index=False)\n",
    "print(\"Trainset saved.\")\n",
    "\n",
    "def cal_retention(group: pd.DataFrame) -> pd.DataFrame:\n",
    "    group['retention'] = round(group['r'].map(lambda x: {1: 0, 2: 1, 3: 1, 4: 1}[x]).mean(), 4)\n",
    "    group['total_cnt'] = group.shape[0]\n",
    "    return group\n",
    "\n",
    "df = df.groupby(by=['r_history', 'delta_t'], group_keys=False).progress_apply(cal_retention)\n",
    "print(\"Retention calculated.\")\n",
    "df = df.drop(columns=['id', 'cid', 'usn', 'ivl', 'last_lvl', 'factor', 'time', 'type', 'create_date', 'review_date', 'real_days', 'r', 't_history'])\n",
    "df.drop_duplicates(inplace=True)\n",
    "df['retention'] = df['retention'].map(lambda x: max(min(0.99, x), 0.01))\n",
    "\n",
    "def cal_stability(group: pd.DataFrame) -> pd.DataFrame:\n",
    "    group_cnt = sum(group['total_cnt'])\n",
    "    if group_cnt < 10:\n",
    "        return pd.DataFrame()\n",
    "    group['group_cnt'] = group_cnt\n",
    "    if group['i'].values[0] > 1:\n",
    "        r_ivl_cnt = sum(group['delta_t'] * group['retention'].map(np.log) * pow(group['total_cnt'], 2))\n",
    "        ivl_ivl_cnt = sum(group['delta_t'].map(lambda x: x ** 2) * pow(group['total_cnt'], 2))\n",
    "        group['stability'] = round(np.log(0.9) / (r_ivl_cnt / ivl_ivl_cnt), 1)\n",
    "    else:\n",
    "        group['stability'] = 0.0\n",
    "    group['avg_retention'] = round(sum(group['retention'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 3)\n",
    "    group['avg_interval'] = round(sum(group['delta_t'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 1)\n",
    "    del group['total_cnt']\n",
    "    del group['retention']\n",
    "    del group['delta_t']\n",
    "    return group\n",
    "\n",
    "df = df.groupby(by=['r_history'], group_keys=False).progress_apply(cal_stability)\n",
    "print(\"Stability calculated.\")\n",
    "df.reset_index(drop = True, inplace = True)\n",
    "df.drop_duplicates(inplace=True)\n",
    "df.sort_values(by=['r_history'], inplace=True, ignore_index=True)\n",
    "\n",
    "if df.shape[0] > 0:\n",
    "    for idx in notebook.tqdm(df.index):\n",
    "        item = df.loc[idx]\n",
    "        index = df[(df['i'] == item['i'] + 1) & (df['r_history'].str.startswith(item['r_history']))].index\n",
    "        df.loc[index, 'last_stability'] = item['stability']\n",
    "    df['factor'] = round(df['stability'] / df['last_stability'], 2)\n",
    "    df = df[(df['i'] >= 2) & (df['group_cnt'] >= 100)]\n",
    "    df['last_recall'] = df['r_history'].map(lambda x: x[-1])\n",
    "    df = df[df.groupby(['i', 'r_history'], group_keys=False)['group_cnt'].transform(max) == df['group_cnt']]\n",
    "    df.to_csv('./stability_for_analysis.tsv', sep='\\t', index=None)\n",
    "    print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "    print(df[df['r_history'].str.contains(r'^[1-4][^124]*$', regex=True)][['r_history', 'avg_interval', 'avg_retention', 'stability', 'factor', 'group_cnt']].to_string(index=False))\n",
    "    print(\"Analysis saved!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Optimize parameter"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Define the model\n",
    "\n",
    "FSRS is a time-series model for predicting memory states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_w = [1, 1, 5, 0.5, 0.5, 0.2, 1.4, 0.12, 0.8, 2, 0.2, 0.2, 1]\n",
    "'''\n",
    "w[0]: initial_stability_for_again_answer\n",
    "w[1]: initial_stability_step_per_rating\n",
    "w[2]: initial_difficulty_for_good_answer\n",
    "w[3]: initial_difficulty_step_per_rating\n",
    "w[4]: next_difficulty_step_per_rating\n",
    "w[5]: next_difficulty_reversion_to_mean_speed (used to avoid ease hell)\n",
    "w[6]: next_stability_factor_after_success\n",
    "w[7]: next_stability_stabilization_decay_after_success\n",
    "w[8]: next_stability_retrievability_gain_after_success\n",
    "w[9]: next_stability_factor_after_failure\n",
    "w[10]: next_stability_difficulty_decay_after_success\n",
    "w[11]: next_stability_stability_gain_after_failure\n",
    "w[12]: next_stability_retrievability_gain_after_failure\n",
    "For more details about the parameters, please see: \n",
    "https://github.com/open-spaced-repetition/fsrs4anki/wiki/Free-Spaced-Repetition-Scheduler\n",
    "'''\n",
    "\n",
    "\n",
    "class FSRS(nn.Module):\n",
    "    def __init__(self, w):\n",
    "        super(FSRS, self).__init__()\n",
    "        self.w = nn.Parameter(torch.tensor(w))\n",
    "\n",
    "    def stability_after_success(self, state, new_d, r):\n",
    "        new_s = state[:,0] * (1 + torch.exp(self.w[6]) *\n",
    "                        (11 - new_d) *\n",
    "                        torch.pow(state[:,0], -self.w[7]) *\n",
    "                        (torch.exp((1 - r) * self.w[8]) - 1))\n",
    "        return new_s\n",
    "    \n",
    "    def stability_after_failure(self, state, new_d, r):\n",
    "        new_s = self.w[9] * torch.pow(new_d, -self.w[10]) * torch.pow(\n",
    "            state[:,0], self.w[11]) * torch.exp((1 - r) * self.w[12])\n",
    "        return new_s\n",
    "\n",
    "    def pow_forgetting_curve(self, t, s):\n",
    "        return (1 + t / (9 * s)) ** -1\n",
    "\n",
    "    def step(self, X, state):\n",
    "        '''\n",
    "        :param X: shape[batch_size, 2], X[:,0] is elapsed time, X[:,1] is rating\n",
    "        :param state: shape[batch_size, 2], state[:,0] is stability, state[:,1] is difficulty\n",
    "        :return state:\n",
    "        '''\n",
    "        if torch.equal(state, torch.zeros_like(state)):\n",
    "            # first learn, init memory states\n",
    "            new_s = self.w[0] + self.w[1] * (X[:,1] - 1)\n",
    "            new_d = self.w[2] - self.w[3] * (X[:,1] - 3)\n",
    "            new_d = new_d.clamp(1, 10)\n",
    "        else:\n",
    "            r = self.pow_forgetting_curve(X[:,0], state[:,0])\n",
    "            new_d = state[:,1] - self.w[4] * (X[:,1] - 3)\n",
    "            new_d = self.mean_reversion(self.w[2], new_d)\n",
    "            new_d = new_d.clamp(1, 10)\n",
    "            condition = X[:,1] > 1\n",
    "            new_s = torch.where(condition, self.stability_after_success(state, new_d, r), self.stability_after_failure(state, new_d, r))\n",
    "        return torch.stack([new_s, new_d], dim=1)\n",
    "\n",
    "    def forward(self, inputs, state=None):\n",
    "        '''\n",
    "        :param inputs: shape[seq_len, batch_size, 2]\n",
    "        '''\n",
    "        if state is None:\n",
    "            state = torch.zeros((inputs.shape[1], 2))\n",
    "        else:\n",
    "            state, = state\n",
    "        outputs = []\n",
    "        for X in inputs:\n",
    "            state = self.step(X, state)\n",
    "            outputs.append(state)\n",
    "        return torch.stack(outputs), state\n",
    "\n",
    "    def mean_reversion(self, init, current):\n",
    "        return self.w[5] * init + (1-self.w[5]) * current\n",
    "\n",
    "\n",
    "class WeightClipper(object):\n",
    "    def __init__(self, frequency=1):\n",
    "        self.frequency = frequency\n",
    "\n",
    "    def __call__(self, module):\n",
    "        if hasattr(module, 'w'):\n",
    "            w = module.w.data\n",
    "            w[0] = w[0].clamp(0.1, 10)\n",
    "            w[1] = w[1].clamp(0.1, 5)\n",
    "            w[2] = w[2].clamp(1, 10)\n",
    "            w[3] = w[3].clamp(0.1, 5)\n",
    "            w[4] = w[4].clamp(0.1, 5)\n",
    "            w[5] = w[5].clamp(0, 0.5)\n",
    "            w[6] = w[6].clamp(0, 2)\n",
    "            w[7] = w[7].clamp(0.01, 0.2)\n",
    "            w[8] = w[8].clamp(0.01, 1.5)\n",
    "            w[9] = w[9].clamp(0.5, 5)\n",
    "            w[10] = w[10].clamp(0.01, 2)\n",
    "            w[11] = w[11].clamp(0.01, 0.9)\n",
    "            w[12] = w[12].clamp(0.01, 2)\n",
    "            module.w.data = w\n",
    "\n",
    "def lineToTensor(line):\n",
    "    ivl = line[0].split(',')\n",
    "    response = line[1].split(',')\n",
    "    tensor = torch.zeros(len(response), 2)\n",
    "    for li, response in enumerate(response):\n",
    "        tensor[li][0] = int(ivl[li])\n",
    "        tensor[li][1] = int(response)\n",
    "    return tensor\n",
    "\n",
    "from torch.utils.data import Dataset, DataLoader, Sampler\n",
    "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence\n",
    "from typing import List\n",
    "\n",
    "class RevlogDataset(Dataset):\n",
    "    def __init__(self, dataframe):\n",
    "        padded = pad_sequence(dataframe['tensor'].to_list(), batch_first=True, padding_value=0)\n",
    "        self.x_train = padded.int()\n",
    "        self.t_train = torch.tensor(dataframe['delta_t'].values, dtype=torch.int)\n",
    "        self.y_train = torch.tensor(dataframe['y'].values, dtype=torch.float)\n",
    "        self.seq_len = torch.tensor(dataframe['tensor'].map(len).values, dtype=torch.long)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.x_train[idx], self.t_train[idx], self.y_train[idx], self.seq_len[idx]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.y_train)\n",
    "    \n",
    "class RevlogSampler(Sampler[List[int]]):\n",
    "    def __init__(self, data_source, batch_size):\n",
    "        self.data_source = data_source\n",
    "        self.batch_size = batch_size\n",
    "        lengths = np.array(data_source.seq_len)\n",
    "        indices = np.argsort(lengths)\n",
    "        full_batches, remainder = divmod(indices.size, self.batch_size)\n",
    "        if full_batches > 0:\n",
    "            self.batch_indices = np.split(indices[:-remainder], full_batches)\n",
    "        else:\n",
    "            self.batch_indices = []\n",
    "        if remainder:\n",
    "            self.batch_indices.append(indices[-remainder:])\n",
    "        self.batch_nums = len(self.batch_indices)\n",
    "        # seed = int(torch.empty((), dtype=torch.int64).random_().item())\n",
    "        seed = 2023\n",
    "        self.generator = torch.Generator()\n",
    "        self.generator.manual_seed(seed)\n",
    "\n",
    "    def __iter__(self):\n",
    "        yield from [self.batch_indices[idx] for idx in torch.randperm(self.batch_nums, generator=self.generator).tolist()]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_source)\n",
    "\n",
    "\n",
    "def collate_fn(batch):\n",
    "    sequences, delta_ts, labels, seq_lens = zip(*batch)\n",
    "    sequences_packed = pack_padded_sequence(torch.stack(sequences, dim=1), lengths=torch.stack(seq_lens), batch_first=False, enforce_sorted=False)\n",
    "    sequences_padded, length = pad_packed_sequence(sequences_packed, batch_first=False)\n",
    "    sequences_padded = torch.as_tensor(sequences_padded)\n",
    "    seq_lens = torch.as_tensor(length)\n",
    "    delta_ts = torch.as_tensor(delta_ts)\n",
    "    labels = torch.as_tensor(labels)\n",
    "    return sequences_padded, delta_ts, labels, seq_lens\n",
    "\n",
    "class Optimizer(object):\n",
    "    def __init__(self, train_set, test_set, n_epoch=1, lr=1e-2, batch_size=256) -> None:\n",
    "        self.model = FSRS(init_w)\n",
    "        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)\n",
    "        self.clipper = WeightClipper()\n",
    "        self.batch_size = batch_size\n",
    "        self.build_dataset(train_set, test_set)\n",
    "        self.n_epoch = n_epoch\n",
    "        self.batch_nums = self.next_train_data_loader.batch_sampler.batch_nums\n",
    "        self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, T_max=self.batch_nums * n_epoch)\n",
    "        self.avg_train_losses = []\n",
    "        self.avg_eval_losses = []\n",
    "        self.loss_fn = nn.BCELoss(reduction='sum')\n",
    "\n",
    "    def build_dataset(self, train_set, test_set):\n",
    "        pre_train_set = train_set[train_set['i'] == 2]\n",
    "        self.pre_train_set = RevlogDataset(pre_train_set)\n",
    "        sampler = RevlogSampler(self.pre_train_set, batch_size=self.batch_size)\n",
    "        self.pre_train_data_loader = DataLoader(self.pre_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        next_train_set = train_set[train_set['i'] > 2]\n",
    "        self.next_train_set = RevlogDataset(next_train_set)\n",
    "        sampler = RevlogSampler(self.next_train_set, batch_size=self.batch_size)\n",
    "        self.next_train_data_loader = DataLoader(self.next_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.train_set = RevlogDataset(train_set)\n",
    "        sampler = RevlogSampler(self.train_set, batch_size=self.batch_size)\n",
    "        self.train_data_loader = DataLoader(self.train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.test_set = RevlogDataset(test_set)\n",
    "        sampler = RevlogSampler(self.test_set, batch_size=self.batch_size)\n",
    "        self.test_data_loader = DataLoader(self.test_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "        print(\"dataset built\")\n",
    "\n",
    "    def train(self):\n",
    "        # pretrain\n",
    "        self.eval()\n",
    "        pbar = notebook.tqdm(desc=\"pre-train\", colour=\"red\", total=len(self.pre_train_data_loader))\n",
    "\n",
    "        for i, batch in enumerate(self.pre_train_data_loader):\n",
    "            self.model.train()\n",
    "            self.optimizer.zero_grad()\n",
    "            sequences, delta_ts, labels, seq_lens = batch\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences)\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = self.model.pow_forgetting_curve(delta_ts, stabilities)\n",
    "            loss = self.loss_fn(retentions, labels)\n",
    "            loss.backward()\n",
    "            self.optimizer.step()\n",
    "            self.model.apply(self.clipper)\n",
    "            pbar.update(n=real_batch_size)\n",
    "\n",
    "        pbar.close()\n",
    "        for name, param in self.model.named_parameters():\n",
    "            print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "\n",
    "        epoch_len = len(self.next_train_data_loader)\n",
    "        pbar = notebook.tqdm(desc=\"train\", colour=\"red\", total=epoch_len*self.n_epoch)\n",
    "        print_len = max(self.batch_nums*self.n_epoch // 10, 1)\n",
    "\n",
    "\n",
    "        for k in range(self.n_epoch):\n",
    "            self.eval()\n",
    "            for i, batch in enumerate(self.next_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = self.model.pow_forgetting_curve(delta_ts, stabilities)\n",
    "                loss = self.loss_fn(retentions, labels)\n",
    "                loss.backward()\n",
    "                for param in self.model.parameters():\n",
    "                    param.grad[:2] = torch.zeros(2)\n",
    "                self.optimizer.step()\n",
    "                self.scheduler.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(real_batch_size)\n",
    "\n",
    "                if (k * self.batch_nums + i + 1) % print_len == 0:\n",
    "                    print(f\"iteration: {k * epoch_len + (i + 1) * self.batch_size}\")\n",
    "                    for name, param in self.model.named_parameters():\n",
    "                        print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "        pbar.close()\n",
    "                \n",
    "        self.eval()\n",
    "\n",
    "        w = list(map(lambda x: round(float(x), 4), dict(self.model.named_parameters())['w'].data))\n",
    "        return w\n",
    "\n",
    "    def eval(self):\n",
    "        self.model.eval()\n",
    "        with torch.no_grad():\n",
    "            sampler = RevlogSampler(self.train_set, batch_size=4096)\n",
    "            train_data_loader = DataLoader(self.train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "            loss = 0\n",
    "            for batch in train_data_loader:\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = self.model.pow_forgetting_curve(delta_ts, stabilities)\n",
    "                loss += self.loss_fn(retentions, labels)\n",
    "            self.avg_train_losses.append(loss/len(train_data_loader))\n",
    "            print(f\"Loss in trainset: {self.avg_train_losses[-1]:.4f}\")\n",
    "\n",
    "            sampler = RevlogSampler(self.test_set, batch_size=4096)\n",
    "            test_data_loader = DataLoader(self.test_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "            loss = 0\n",
    "            for batch in test_data_loader:\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = self.model.pow_forgetting_curve(delta_ts, stabilities)\n",
    "                loss += self.loss_fn(retentions, labels)\n",
    "            self.avg_eval_losses.append(loss/len(test_data_loader))\n",
    "            print(f\"Loss in testset: {self.avg_eval_losses[-1]:.4f}\")\n",
    "\n",
    "    def plot(self):\n",
    "        plt.plot(self.avg_train_losses, label='train')\n",
    "        plt.plot(self.avg_eval_losses, label='test')\n",
    "        plt.xlabel('epoch')\n",
    "        plt.ylabel('loss')\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Train the model\n",
    "\n",
    "The [./revlog_history.tsv](./revlog_history.tsv) generated before will be used for training the FSRS model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5c537e6405fc412989a0f41dbf29fc9e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/223795 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorized!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/fsrs4anki/lib/python3.8/site-packages/sklearn/model_selection/_split.py:885: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 147736 TEST: 76059\n",
      "dataset built\n",
      "Loss in trainset: 0.3499\n",
      "Loss in testset: 0.3165\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "82de9ef2f13e4c28a244f7f463d9ba25",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/17371 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [0.8036, 1.9976, 5.0, 0.5, 0.5, 0.2, 1.4, 0.12, 0.8, 2.0, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "91698d2700b94f50a4a330db179f12ef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/391095 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3482\n",
      "Loss in testset: 0.3118\n",
      "iteration: 39168\n",
      "w: [0.8884, 2.0385, 5.1006, 1.3706, 1.1202, 0.0533, 1.3967, 0.0824, 0.9212, 1.7469, 0.5266, 0.5119, 1.1088]\n",
      "iteration: 78336\n",
      "w: [0.8884, 2.0385, 4.8397, 1.3959, 1.213, 0.0359, 1.3195, 0.1303, 0.8724, 1.7935, 0.484, 0.526, 1.2347]\n",
      "iteration: 117504\n",
      "w: [0.8884, 2.0385, 4.8517, 1.4855, 1.4167, 0.0133, 1.3488, 0.1569, 0.9465, 1.7723, 0.5057, 0.4889, 1.4965]\n",
      "Loss in trainset: 0.3280\n",
      "Loss in testset: 0.2912\n",
      "iteration: 156477\n",
      "w: [0.8884, 2.0385, 4.7458, 1.7135, 1.2085, 0.0001, 1.3596, 0.1772, 0.9651, 1.8059, 0.4592, 0.5414, 1.3139]\n",
      "iteration: 195645\n",
      "w: [0.8884, 2.0385, 4.6755, 1.588, 1.325, 0.0035, 1.2859, 0.1599, 0.8988, 1.6512, 0.6192, 0.6135, 1.309]\n",
      "iteration: 234813\n",
      "w: [0.8884, 2.0385, 4.4743, 1.3275, 1.2261, 0.0, 1.2605, 0.1339, 0.8767, 1.7062, 0.5538, 0.5455, 1.4002]\n",
      "Loss in trainset: 0.3274\n",
      "Loss in testset: 0.2904\n",
      "iteration: 273786\n",
      "w: [0.8884, 2.0385, 4.4711, 1.3306, 1.2201, 0.0267, 1.2813, 0.08, 0.8948, 1.7342, 0.5181, 0.6459, 1.3595]\n",
      "iteration: 312954\n",
      "w: [0.8884, 2.0385, 4.4603, 1.2838, 1.239, 0.0191, 1.2599, 0.079, 0.8693, 1.6929, 0.5509, 0.5835, 1.3332]\n",
      "iteration: 352122\n",
      "w: [0.8884, 2.0385, 4.4622, 1.3017, 1.2374, 0.0136, 1.2472, 0.0761, 0.853, 1.7013, 0.5404, 0.6251, 1.3273]\n",
      "iteration: 391290\n",
      "w: [0.8884, 2.0385, 4.4656, 1.306, 1.2443, 0.005, 1.2468, 0.0792, 0.8522, 1.7077, 0.5332, 0.6271, 1.3296]\n",
      "Loss in trainset: 0.3272\n",
      "Loss in testset: 0.2903\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 149817 TEST: 73978\n",
      "dataset built\n",
      "Loss in trainset: 0.3410\n",
      "Loss in testset: 0.3336\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d02b597f4f54eb087a3f7523c0def8e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/19836 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [0.7428, 2.638, 5.0, 0.5, 0.5, 0.2, 1.4, 0.12, 0.8, 2.0, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "15049458e18f4f17b73cdb62943b359b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/389943 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3370\n",
      "Loss in testset: 0.3314\n",
      "iteration: 38912\n",
      "w: [0.5954, 2.6646, 4.9719, 1.0566, 1.092, 0.0338, 1.3233, 0.0651, 0.9766, 1.7577, 0.5312, 0.4689, 1.1692]\n",
      "iteration: 77824\n",
      "w: [0.5954, 2.6646, 4.8474, 1.4565, 1.235, 0.0, 1.3314, 0.0792, 1.0194, 1.7843, 0.5372, 0.5776, 1.3703]\n",
      "iteration: 116736\n",
      "w: [0.5954, 2.6646, 4.5078, 1.19, 1.2113, 0.0026, 1.1668, 0.1254, 0.8942, 1.796, 0.5627, 0.4703, 1.3074]\n",
      "Loss in trainset: 0.3169\n",
      "Loss in testset: 0.3105\n",
      "iteration: 155581\n",
      "w: [0.5954, 2.6646, 4.2702, 1.123, 1.2824, 0.0057, 1.1431, 0.0894, 0.9031, 1.87, 0.4873, 0.556, 1.6358]\n",
      "iteration: 194493\n",
      "w: [0.5954, 2.6646, 4.2365, 1.1832, 1.3525, 0.0304, 1.0635, 0.0638, 0.8362, 1.8403, 0.516, 0.6773, 1.5093]\n",
      "iteration: 233405\n",
      "w: [0.5954, 2.6646, 4.1992, 1.2358, 1.3249, 0.0, 1.1215, 0.1138, 0.8987, 1.8463, 0.5155, 0.5881, 1.4549]\n",
      "Loss in trainset: 0.3171\n",
      "Loss in testset: 0.3105\n",
      "iteration: 272250\n",
      "w: [0.5954, 2.6646, 4.1742, 1.2948, 1.3377, 0.0177, 1.1496, 0.0757, 0.9278, 1.8605, 0.5001, 0.5598, 1.3923]\n",
      "iteration: 311162\n",
      "w: [0.5954, 2.6646, 4.1716, 1.3756, 1.2759, 0.0015, 1.1025, 0.0873, 0.881, 1.8289, 0.5344, 0.6304, 1.3442]\n",
      "iteration: 350074\n",
      "w: [0.5954, 2.6646, 4.1554, 1.3387, 1.3014, 0.0089, 1.1239, 0.0672, 0.901, 1.8174, 0.5419, 0.605, 1.3233]\n",
      "iteration: 388986\n",
      "w: [0.5954, 2.6646, 4.1477, 1.3287, 1.2969, 0.0081, 1.1249, 0.0691, 0.9019, 1.8214, 0.5369, 0.6059, 1.3288]\n",
      "Loss in trainset: 0.3167\n",
      "Loss in testset: 0.3102\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 150037 TEST: 73758\n",
      "dataset built\n",
      "Loss in trainset: 0.3249\n",
      "Loss in testset: 0.3664\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc83164b04534b0ebd366183e41330dc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/20733 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [2.095, 2.2186, 5.0, 0.5, 0.5, 0.2, 1.4, 0.12, 0.8, 2.0, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "965e310bb86d4b0085b695d14690923c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/387912 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3216\n",
      "Loss in testset: 0.3715\n",
      "iteration: 38656\n",
      "w: [2.2297, 2.3606, 5.432, 1.7988, 1.3584, 0.0253, 1.3226, 0.1764, 0.9177, 1.7841, 0.5603, 0.6901, 1.391]\n",
      "iteration: 77312\n",
      "w: [2.2297, 2.3606, 5.055, 1.6489, 1.39, 0.0517, 1.2173, 0.0184, 0.8176, 1.7716, 0.5739, 0.7108, 1.6304]\n",
      "iteration: 115968\n",
      "w: [2.2297, 2.3606, 4.8404, 1.4478, 1.5167, 0.0418, 1.3005, 0.0118, 0.9254, 1.8472, 0.5329, 0.5565, 1.707]\n",
      "Loss in trainset: 0.3020\n",
      "Loss in testset: 0.3533\n",
      "iteration: 154392\n",
      "w: [2.2297, 2.3606, 4.7497, 1.6563, 1.4757, 0.0392, 1.2479, 0.0992, 0.9049, 1.8577, 0.5447, 0.6509, 1.7714]\n",
      "iteration: 193048\n",
      "w: [2.2297, 2.3606, 4.6737, 1.8045, 1.1914, 0.0551, 1.2281, 0.0444, 0.8796, 1.7703, 0.6279, 0.6501, 1.7031]\n",
      "iteration: 231704\n",
      "w: [2.2297, 2.3606, 4.7052, 1.5446, 1.5841, 0.0542, 1.1842, 0.044, 0.8385, 1.8108, 0.5808, 0.6202, 1.7938]\n",
      "Loss in trainset: 0.3010\n",
      "Loss in testset: 0.3522\n",
      "iteration: 270128\n",
      "w: [2.2297, 2.3606, 4.6933, 1.6156, 1.5381, 0.0337, 1.1471, 0.0296, 0.7976, 1.7577, 0.6301, 0.5868, 1.6405]\n",
      "iteration: 308784\n",
      "w: [2.2297, 2.3606, 4.6414, 1.5719, 1.5498, 0.0229, 1.1702, 0.0107, 0.8118, 1.8026, 0.5803, 0.6501, 1.6644]\n",
      "iteration: 347440\n",
      "w: [2.2297, 2.3606, 4.6321, 1.5665, 1.5391, 0.0149, 1.1738, 0.0273, 0.8135, 1.8018, 0.5793, 0.6648, 1.6675]\n",
      "iteration: 386096\n",
      "w: [2.2297, 2.3606, 4.6341, 1.567, 1.541, 0.0146, 1.1719, 0.0286, 0.8112, 1.8034, 0.5774, 0.6616, 1.6657]\n",
      "Loss in trainset: 0.3002\n",
      "Loss in testset: 0.3515\n"
     ]
    },
    {
     "data": {
      "image/png": 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GDWPo0KFce+21NGnSxIRqq6YAVJ/yd8JvX7s/9ws6Yc+dsrU7Ea3NrU8aJosFBv7NHYo/SnEvfH/jj3DZy9DrBrOrE5Ga8g92j8SY9b1r6PDhw1x++eU899xzFV5r2bIlNpuNJUuW8N133/HFF18wZcoUxo8fz8qVK+nQocPZVF2rFIDq0zl/Anuo+xdZi+7uO4WLVFe7/u4bqs67DX5bCh/fCdu+gz89D/7aVl/E61gsXjHKb7fbcTqPL9Ho3bs3c+fOpX379vj5Vf57zGKxMHDgQAYOHMiECRNo164dH3/8MampqRXOZxYtJqlPzc+BvrdCy54KP1Izoc3hr/Ng8DjAApkz4d9JsG+z2ZWJSCPVvn17Vq5cydatW9m7dy/33HMP+/fv58Ybb2T16tVs3ryZzz//nJSUFJxOJytXruSZZ55hzZo1bN++nXnz5rFnzx66devmOd+6devIzs5m7969lJScvFFv/VAAEvE2Vpv7vmE3z4Pgpu6dvv81GH5aYHZlItIIPfzww9hsNrp3707z5s0pLi5mxYoVOJ1Ohg4dSo8ePXjggQeIjIzEarUSHh7ON998w6WXXso555zD//3f//Hiiy/ypz/9CYDbb7+dLl26kJCQQPPmzVmxYoUp/dJl8JXwybvBi3cq2AUfpsCO791fXzAGkp4Ev+pfUioi9UN3g68dtXUZvEaARLxZeCsY/SkMuM/99fevwTuXuhfci4hIlRSARLydzR+G/sN9uXxghHt/qRl/gF+/NLsyEZEGSwFIpLHoOsK9cWLLePcNcf9zLXz1D3CZf7WFiEhDowAk0pg0aQ+3fA4JtwIGfPMCvHslHMo1uzIRkQZFAUiksfEPhMtegj+/Cf4hsPVbeP0PsHW52ZWJCKBrj85Obf35KQCJNFY9r4M7lkLzbnA4F2ZeDt++6L4XnYjUO5vNBkBxcbHJlXi3oiL3DWT9/f3P6jzajU+kMWt+DtyeDgsfgv+9B+lPwfaVcPUMCI4yuzoRn+Ln50dwcDB79uzB398fq25sfUYMw6CoqIi8vDwiIyM9gbKmtA9QJbQPkDQ6hgGZ78KiR8DpgIhYuG4mtOljdmUiPqW4uJgtW7bg0khsjUVGRhITE4PFYqnw2pn8/lYAqoQCkDRau9fBByPhwBaw+sOwp6HfHe57EolIvXC5XJoGqyF/f/9TjvwoAJ0lBSBp1I7mwyf3wsayW2d0vwqumAKB+rsuIt5NO0GLSNUCI+D6d2H4s2D1g5/mu+8llvOj2ZWJiNQbBSARX2SxwAV3Q8piCG8D+zfDm5dA5iyzKxMRqRcKQCK+LLYv3PUtxA2B0qOw4F6YPwaKi8yuTESkTikAifi64Cj4ywfwx8fBYoWs/7hHg/b+anZlIiJ1RgFIRMBqhUEPw8hPIKQF5P3kXhf041yzKxMRqRMKQCJyXIdB7imxdhdC8WH46BZY+DCUOsyuTESkVikAiUh5YTHukaA/POT+evUb8NZwOLDN3LpERGqRApCIVGTzg0smwF8+hKAmsCvTfUPV7M/MrkxEpFYoAIlI1c4ZCnd+A60T3BsovncDLJkAzlKzKxMROSsKQCJyapFtIeUzSLzL/fWKV9x3li/YbW5dIiJnQQFIRE7Pzw5/eg6uewfsYbD9O/eU2G9Lza5MRKRGFIBEpPrOvRruWArR50HhHnj3Klj2POjO1iLiZUwPQNOmTaN9+/YEBgaSmJjIqlWrqmw7b948EhISiIyMJCQkhPj4eGbNqrh1/8aNG7niiiuIiIggJCSEvn37sn379rrshojvaBYHt30J598MGPD10/Cfa6Bwr9mViYhUm6kBaM6cOaSmpjJx4kQyMzPp1asXw4YNIy8vr9L2UVFRjB8/noyMDNatW0dKSgopKSl8/vnnnjabN2/mwgsvpGvXrixdupR169bx+OOPExgYWF/dEmn8/IPgyqlw1XTwC4LNX8GMP8D2lWZXJiJSLRbDMAyzvnliYiJ9+/Zl6tSpALhcLmJjY7nvvvsYO3Zstc7Ru3dvRowYwaRJkwC44YYb8Pf3r3RkqCoOhwOH4/hGbwUFBcTGxpKfn094ePgZ9EjEB+VugA9Gwb5f3XeXT3oS+t/jvuGqiEg9KigoICIiolq/v00bASouLmbt2rUkJSUdL8ZqJSkpiYyMjNMebxgG6enpZGdnM2jQIMAdoBYuXMg555zDsGHDaNGiBYmJicyfP/+U50pLSyMiIsLziI2NPau+ifiU6HPhjq/hvGvAVQpfjIc5f4UjB82uTESkSqYFoL179+J0OomOji73fHR0NDk5OVUel5+fT2hoKHa7nREjRjBlyhSGDBkCQF5eHocPH+bZZ59l+PDhfPHFF1x99dX8+c9/ZtmyZVWec9y4ceTn53seO3bsqJ1OiviKgDC45t9w6T/BZoefP4V/XQS7ssyuTESkUn5mF3CmwsLCyMrK4vDhw6Snp5OamkrHjh0ZPHgwrrIrUa688koefPBBAOLj4/nuu++YMWMGF110UaXnDAgIICAgoN76INIoWSzQ73Zo3Qc+HAUHtsK/h8KfnoU+KZoSE5EGxbQRoGbNmmGz2cjNzS33fG5uLjExMVUeZ7VaiYuLIz4+noceeohrr72WtLQ0zzn9/Pzo3r17uWO6deumq8BE6kvr3u7do8/5Ezgd8OmDMO8OcBw2uzIREQ/TApDdbqdPnz6kp6d7nnO5XKSnp9O/f/9qn8flcnkWMNvtdvr27Ut2dna5Nr/88gvt2rWrncJF5PSCmsCN78GQp8Big/UfwBt/hLyfza5MRAQweQosNTWVUaNGkZCQQL9+/Zg8eTKFhYWkpKQAMHLkSFq3bu0Z4UlLSyMhIYFOnTrhcDhYtGgRs2bNYvr06Z5zPvLIIyQnJzNo0CAuvvhiFi9ezH//+1+WLl1qRhdFfJfFAgPvhzZ94aNbYG82vHExXDYZeiWbXZ2I+DhTA1BycjJ79uxhwoQJ5OTkEB8fz+LFiz0Lo7dv347VenyQqrCwkDFjxrBz506CgoLo2rUrs2fPJjn5+D+mV199NTNmzCAtLY2//e1vdOnShblz53LhhRfWe/9EBGg3AO78FubeCluWwcd3uG+lMfw58Nf+XCJiDlP3AWqozmQfARGpJpcTlj3nvnUGBsT0gOvfhaiOZlcmIo2EV+wDJCI+xmqDi/8Of50LwU0hZz28fhH8tMDsykTEBykAiUj9irvEPSUWmwiOAvjgZlj8d3CWmF2ZiPgQBSARqX8RrWH0Quh/r/vr76fB25dC/k5z6xIRn6EAJCLmsPnDsKch+T8QEAE7V7lvqPrrl2ZXJiI+QAFIRMzV7TK4cxm07AVH9sN/roWv/uFeNC0iUkcUgETEfFEd4JYvIOEWwIBvXoBZV8HhPLMrE5FGSgFIRBoG/0C47GX485vgHwJbvnFPiW1dYXZlItIIKQCJSMPS8zq442to3hUO58DMy+Dbl6DsZsciIrVBAUhEGp7mXeD2r6DnDWC4IP1JeP9GKNpvdmUi0kgoAIlIw2QPgatnwOWvgC0Aflns3jhx51qzKxORRkABSEQaLosF+oyG25ZAkw6Qvx3eGgYr/wW6i4+InAUFIBFp+Fr2cl8q3/UycJXAZ4/ARylwtMDsykTESykAiYh3CIyA5NkwLA2sfrDhY/jXYMj50ezKRMQLKQCJiPewWKD/GEj5DMJbw/7N8OYl8MNssysTES+jACQi3ie2n/uGqnFJUHoUPrkH5t8DxUVmVyYiXkIBSES8U0hT+MuH8Mf/A4sVsmbDm0mwd5PZlYmIF1AAEhHvZbXCoEfg5vkQ0gLyNsC/LoIf55ldmYg0cApAIuL9Ol4Ed30L7QZC8WH3FWKLHoFSh9mViUgDpQAkIo1DWAyMXAAXPuj+etW/4K3hcGCbuXWJSIOkACQijYfND5KegL98AIGRsCsTXh8E2YvNrkxEGhgFIBFpfM4Z5p4Sa90Hjh6E95JhyURwlppdmYg0EApAItI4RbaFlMXQ70731ysmw7tXQMFuU8sSkYbBYhi6oc7JCgoKiIiIID8/n/DwcLPLEZGzteFj+OQ+KD4EIc0h6UkIbwn20LJHiPtjQCjY7O4NF0XE65zJ72+/eqpJRMQ8514N0T3gg5HuS+U/GVN1W6tfWSAKK/sY4g5G5cJSCASc8Pop24a61yaJSIOin0oR8Q3N4uC2L2HpM7BjFTgOuy+ZLy50fyw96m7nKoWj+e5HbfELPE1YCjkemgJCq2h7wuf+Ie49kESkxhSARMR32INh6D8qf81ZCiWFZcGoLBR5AlIhOA5VfP6UbQ+7wxS4w1XpUSjaV3t98T8xRFUjWJVre+JzZcf5BWrqT3yKApCICLinqWwR7rvO15bS4uPhqNKwdOLzhe41Sp4QdVK7Yx8Nl/vcJYXuR2Fe7dRqsZ00xXdyiDp52u/kYHVSW3so+NlrpzaROqAAJCJSV/zs4BcFwVG1cz7DgJIj1QhLJ49QnaJtSdkNZA0nOPLdj9pis1djdOqEBejVCVZWW+3VJz5NAUhExFtYLO5pPHsw0Lx2zulyukNQZSNUjhOCU4VgVcno1LFzOMtuQeIshiPFcORA7dQK4BfkDkZ+QWUBMxD8AsAW4P7oF3iK50/43GY/6bljz5/mPApgjYYCkIiIL7Pa3FNbAWG1d05nyWnWSVU1JXjyWqsT2hpO97lLj7gfZrH6VzNIHXv+VIHshDZnch6rn9Zr1QIFIBERqV02fwhq4n7UBsNw39j22HSe47D7a6ejbIG544THUffI07GF56XFx9s4T2hT4fkqzlNyBDhhuzxXCRSXBTyzWKynGak6VZA6kxGv05zHy0OYApCIiDRsFgv4B7ofIU3r93sbhvtqvnLB6FRB6uQwdWIgOzlgVec8Zc+7Sk6oyWX+SBiUD0PVmoI8acSr/YUQd4lp5SsAiYiIVMVicY9o2fzda4/M4nLV0ohXFYGsWqNjR8vX5Cx2Pxxn0S8FIBEREamS1QrWIPAPMq8Gw3Cv76o0RNUgdMUmmtcXFIBERESkOiyWsmmsxrG/k/ZSFxEREZ/TIALQtGnTaN++PYGBgSQmJrJq1aoq286bN4+EhAQiIyMJCQkhPj6eWbNmlWszevRoLBZLucfw4cPruhsiIiLiJUyfApszZw6pqanMmDGDxMREJk+ezLBhw8jOzqZFixYV2kdFRTF+/Hi6du2K3W7n008/JSUlhRYtWjBs2DBPu+HDh/P22297vg4ICKiX/oiIiEjDZzEMwzh9s7qTmJhI3759mTp1KgAul4vY2Fjuu+8+xo4dW61z9O7dmxEjRjBp0iTAPQJ08OBB5s+fX63jHQ4HDsfxZewFBQXExsaSn59PeHj4mXVIRERETFFQUEBERES1fn+bOgVWXFzM2rVrSUpK8jxntVpJSkoiIyPjtMcbhkF6ejrZ2dkMGjSo3GtLly6lRYsWdOnShbvvvpt9+6q+C3NaWhoRERGeR2xsbM07JSIiIg2eqQFo7969OJ1OoqOjyz0fHR1NTk5Olcfl5+cTGhqK3W5nxIgRTJkyhSFDhnheHz58OO+++y7p6ek899xzLFu2jD/96U84nc5Kzzdu3Djy8/M9jx07dtROB0VERKRBMn0NUE2EhYWRlZXF4cOHSU9PJzU1lY4dOzJ48GAAbrjhBk/bHj160LNnTzp16sTSpUu55JKKmy4FBARojZCIiIgPMTUANWvWDJvNRm5ubrnnc3NziYmJqfI4q9VKXFwcAPHx8WzcuJG0tDRPADpZx44dadasGZs2bao0AImIiIhvMXUKzG6306dPH9LT0z3PuVwu0tPT6d+/f7XP43K5yi1iPtnOnTvZt28fLVu2PKt6RUREpHEwfQosNTWVUaNGkZCQQL9+/Zg8eTKFhYWkpKQAMHLkSFq3bk1aWhrgXrCckJBAp06dcDgcLFq0iFmzZjF9+nQADh8+zJNPPsk111xDTEwMmzdv5tFHHyUuLq7cZfIiIiLiu0wPQMnJyezZs4cJEyaQk5NDfHw8ixcv9iyM3r59O1br8YGqwsJCxowZw86dOwkKCqJr167Mnj2b5ORkAGw2G+vWrWPmzJkcPHiQVq1aMXToUCZNmqR1PiIiIgI0gH2AGqIz2UdAREREGgav2QdIRERExAwKQCIiIuJzFIBERETE5ygAiYiIiM9RABIRERGfowAkIiIiPkcBSERERHyOApCIiIj4HAUgERER8TkKQCIiIuJzFIBERETE5ygAiYiIiM9RABIRERGfowAkIiIiPkcBSERERHyOApCIiIj4HAUgERER8TkKQCIiIuJzFIBERETE5ygAiYiIiM9RABIRERGfowAkIiIiPkcBSERERHyOApCIiIj4HAUgERER8TkKQCIiIuJzFIBERETE5ygAiYiIiM9RABIRERGfowAkIiIiPkcBSERERHyOApCIiIj4HAUgERER8TkKQCIiIuJzFIBERETE5ygAiYiIiM9pEAFo2rRptG/fnsDAQBITE1m1alWVbefNm0dCQgKRkZGEhIQQHx/PrFmzqmx/1113YbFYmDx5ch1ULiIiIt7I9AA0Z84cUlNTmThxIpmZmfTq1Ythw4aRl5dXafuoqCjGjx9PRkYG69atIyUlhZSUFD7//PMKbT/++GO+//57WrVqVdfdEBERES9iegB66aWXuP3220lJSaF79+7MmDGD4OBg3nrrrUrbDx48mKuvvppu3brRqVMn7r//fnr27Mny5cvLtfv999+57777+M9//oO/v399dEVERES8hKkBqLi4mLVr15KUlOR5zmq1kpSUREZGxmmPNwyD9PR0srOzGTRokOd5l8vFzTffzCOPPMK555572vM4HA4KCgrKPURERKTxMjUA7d27F6fTSXR0dLnno6OjycnJqfK4/Px8QkNDsdvtjBgxgilTpjBkyBDP68899xx+fn787W9/q1YdaWlpREREeB6xsbE165CIiIh4BT+zC6iJsLAwsrKyOHz4MOnp6aSmptKxY0cGDx7M2rVreeWVV8jMzMRisVTrfOPGjSM1NdXzdUFBgUKQiIhII2ZqAGrWrBk2m43c3Nxyz+fm5hITE1PlcVarlbi4OADi4+PZuHEjaWlpDB48mG+//Za8vDzatm3rae90OnnooYeYPHkyW7durXC+gIAAAgICaqdTIiIi0uCZOgVmt9vp06cP6enpnudcLhfp6en079+/2udxuVw4HA4Abr75ZtatW0dWVpbn0apVKx555JFKrxQTERER32P6FFhqaiqjRo0iISGBfv36MXnyZAoLC0lJSQFg5MiRtG7dmrS0NMC9XichIYFOnTrhcDhYtGgRs2bNYvr06QA0bdqUpk2blvse/v7+xMTE0KVLl/rtnIiIiDRIpgeg5ORk9uzZw4QJE8jJySE+Pp7Fixd7FkZv374dq/X4QFVhYSFjxoxh586dBAUF0bVrV2bPnk1ycrJZXRAREREvYzEMwzC7iIamoKCAiIgI8vPzCQ8PN7scERERqYYz+f1t+kaIIiIiIvVNAUhERER8jgKQiIiI+JwaBaCZM2eycOFCz9ePPvookZGRDBgwgG3bttVacSIiIiJ1oUYB6JlnniEoKAiAjIwMpk2bxvPPP0+zZs148MEHa7VAERERkdpWo8vgd+zY4dmJef78+VxzzTXccccdDBw4kMGDB9dmfSIiIiK1rkYjQKGhoezbtw+AL774wnMj0sDAQI4cOVJ71YmIiIjUgRqNAA0ZMoTbbruN888/n19++YVLL70UgA0bNtC+ffvarE9ERESk1tVoBGjatGn079+fPXv2MHfuXM+tJ9auXcuNN95YqwWKiIiI1DbtBF0J7QQtIiLifep8J+jFixezfPlyz9fTpk0jPj6ev/zlLxw4cKAmpxQRERGpNzUKQI888ggFBQUArF+/noceeohLL72ULVu2kJqaWqsFioiIiNS2Gi2C3rJlC927dwdg7ty5XHbZZTzzzDNkZmZ6FkSLiIiINFQ1GgGy2+0UFRUB8OWXXzJ06FAAoqKiPCNDIiIiIg1VjUaALrzwQlJTUxk4cCCrVq1izpw5APzyyy+0adOmVgsUERERqW01GgGaOnUqfn5+fPTRR0yfPp3WrVsD8NlnnzF8+PBaLVBERESktuky+EroMngRERHvcya/v2s0BQbgdDqZP38+GzduBODcc8/liiuuwGaz1fSUIiIiIvWiRgFo06ZNXHrppfz+++906dIFgLS0NGJjY1m4cCGdOnWq1SJFREREalON1gD97W9/o1OnTuzYsYPMzEwyMzPZvn07HTp04G9/+1tt1ygiIiJSq2o0ArRs2TK+//57oqKiPM81bdqUZ599loEDB9ZacSIiIiJ1oUYjQAEBARw6dKjC84cPH8Zut591USIiIiJ1qUYB6LLLLuOOO+5g5cqVGIaBYRh8//333HXXXVxxxRW1XaOIiIhIrapRAHr11Vfp1KkT/fv3JzAwkMDAQAYMGEBcXByTJ0+u5RJFREREaleN1gBFRkbyySefsGnTJs9l8N26dSMuLq5WixMRERGpC9UOQKe7y/vXX3/t+fyll16qeUUiIiIidazaAeiHH36oVjuLxVLjYkRERETqQ7UD0IkjPCIiIiLerEaLoEVERES8mQKQiIiI+BwFIBEREfE5CkAiIiLicxSARERExOcoAImIiIjPUQASERERn6MAJCIiIj6nQQSgadOm0b59ewIDA0lMTGTVqlVVtp03bx4JCQlERkYSEhJCfHw8s2bNKtfmiSeeoGvXroSEhNCkSROSkpJYuXJlXXdDREREvITpAWjOnDmkpqYyceJEMjMz6dWrF8OGDSMvL6/S9lFRUYwfP56MjAzWrVtHSkoKKSkpfP75554255xzDlOnTmX9+vUsX76c9u3bM3ToUPbs2VNf3RIREZEGzGIYhmFmAYmJifTt25epU6cC4HK5iI2N5b777mPs2LHVOkfv3r0ZMWIEkyZNqvT1goICIiIi+PLLL7nkkktOe75j7fPz8wkPD69+Z0RERMQ0Z/L729QRoOLiYtauXUtSUpLnOavVSlJSEhkZGac93jAM0tPTyc7OZtCgQVV+j3/9619ERETQq1evSts4HA4KCgrKPURERKTxMjUA7d27F6fTSXR0dLnno6OjycnJqfK4/Px8QkNDsdvtjBgxgilTpjBkyJBybT799FNCQ0MJDAzk5ZdfZsmSJTRr1qzS86WlpREREeF5xMbGnn3nREREpMEyfQ1QTYSFhZGVlcXq1at5+umnSU1NZenSpeXaXHzxxWRlZfHdd98xfPhwrr/++irXFY0bN478/HzPY8eOHfXQCxERETGLn5nfvFmzZthsNnJzc8s9n5ubS0xMTJXHWa1W4uLiAIiPj2fjxo2kpaUxePBgT5uQkBDi4uKIi4vjggsuoHPnzvz73/9m3LhxFc4XEBBAQEBA7XRKREREGjxTR4Dsdjt9+vQhPT3d85zL5SI9PZ3+/ftX+zwulwuHw3HWbURERMQ3mDoCBJCamsqoUaNISEigX79+TJ48mcLCQlJSUgAYOXIkrVu3Ji0tDXCv10lISKBTp044HA4WLVrErFmzmD59OgCFhYU8/fTTXHHFFbRs2ZK9e/cybdo0fv/9d6677jrT+ikiIiINh+kBKDk5mT179jBhwgRycnKIj49n8eLFnoXR27dvx2o9PlBVWFjImDFj2LlzJ0FBQXTt2pXZs2eTnJwMgM1m4+eff2bmzJns3buXpk2b0rdvX7799lvOPfdcU/ooIiIiDYvp+wA1RNoHSERExPt4zT5AIiIiImZQABIRERGfowAkIiIiPkcBSERERHyOApCIiIj4HAUgERER8TkKQCIiIuJzFIBERETE5ygAiYiIiM9RABIRERGfowAkIiIiPkcBSERERHyOApCIiIj4HAUgERER8TkKQCIiIuJz/MwuwJf8nFPA2m0HaBcVQrumwbSMCMTPpgwqIiJS3xSA6tHS7D08+9nPnq/9rBZaNwmibVQw7ZoG0y4qhLZN3Z+3jQom2K63R0REpC7oN2w9atMkiEu6tmDb/iK27y+iuNTFtn1FbNtXxLe/VmzfPCyAdlHB7lBUNmrk/jyYqBA7Foul/jshIiLSCFgMwzDMLqKhKSgoICIigvz8fMLDw+vke7hcBrmHjrJtXxHb9xWxbX+h+/P97kCUf6TklMeHBvh5Ro7KBaQoTa2JiIhvOpPf3wpAlaiPAHQ6+UUlJ4Wi45/nFBzlVO+an9VCmyZBtG0aQruo41Nq7ZqG0DYqmCC7rf46IiIiUk/O5Pe3psAaqIhgf3oGR9KzTWSF146WONl5oMgzfbZ9//GQtGP/EYqdLrbuK2LrvqJKz90iLKAsFLlHjU4MSE2C/TW1JiIijZ4CkBcK9LcR1yKMuBZhFV5zuQxyCsqm1spGkLbtL5tm21dIwdFS8g45yDvkYPXWAxWODwvwO2Eh9vFptbZRwbSKDMJmVTgSERHvpymwSjSEKbC6crCo+IRQVD4g5RQcPeWx/jYLbZoEH197VDZqdOzzQH9NrYmIiHk0BSZVigy2Exlsp1dsZIXXjpY42VG2CHvb/qKyzwvZtr+InWVTa1v2FrJlb2Gl544ODzh+Kf+xq9fK1iFFampNREQaEI0AVaIxjwDVlNMztVZYdtVa+avXDh0tPeXxYYF+nr2OYj37HrlDUssITa2JiMjZ01VgZ0kB6MwYhsHBohK2lY0YnRyQcgscpzzebrOWXbV2LBQdv3otVlNrIiJSTZoCk3plsVhoEmKnSYid+Cqm1o7tb7T92NqjsoC040ARxU4Xv+0t5LcqptZiwgM94ci979HxgBQZbK/j3omISGOkEaBKaASo/jhdBrvzj3hGjU68em37viIOOU49tRYe6OfZCPLktUctwwOxampNRMRnaArsLCkANQyGYXCgqMQ9rbb/xH2P3AEp71A1ptaigspGi0KO33OtaTBtmmhqTUSksdEUmDQKFouFqBA7USF2zm/bpMLrR4qd5TaBPHGabeexqbU9hfy2pxDYc9K5y6bWPKEopNxNaSOC/euplyIiYgaNAFVCI0Dez+ky2HXwyPGRo/1li7PLAtLhakyttWsaUn7tUdnGkDGaWhMRaZA0BXaWFIAaN8Mw2F9YfMIO2ScEpP1F7DnN1FqTYH/S/tyT4efF1FPFIiJSHQpAZ0kByLcVFZeyY/+R8muPyq5e23ngCKUu949M6pBzuO+PcdrgUUSkgdAaIJGzEGz3o0tMGF1iKt5rrcTp4plFG3l7xVZeWvIL2TmHeOG6ngTb9aMkIuJNrGYXIOJN/G1WJl5+Ls9d0wN/m4WF63dz7fQMfj94xOzSRETkDCgAidRAct+2/L/bL6BpiJ2fdhdwxZTlrNm63+yyRESkmhpEAJo2bRrt27cnMDCQxMREVq1aVWXbefPmkZCQQGRkJCEhIcTHxzNr1izP6yUlJTz22GP06NGDkJAQWrVqxciRI9m1a1d9dEV8SN/2USy470K6twxnX2ExN77xPR+s3mF2WSIiUg2mB6A5c+aQmprKxIkTyczMpFevXgwbNoy8vLxK20dFRTF+/HgyMjJYt24dKSkppKSk8PnnnwNQVFREZmYmjz/+OJmZmcybN4/s7GyuuOKK+uyW+IjWkUF8dHd/Lu0RQ4nT4NG563jyvxsodbrMLk1ERE7B9KvAEhMT6du3L1OnTgXA5XIRGxvLfffdx9ixY6t1jt69ezNixAgmTZpU6eurV6+mX79+bNu2jbZt2572fLoKTM6UYRi8mr6Jl7/8BYA/dG7G1Bt7a0NFEZF6dCa/v00dASouLmbt2rUkJSV5nrNarSQlJZGRkXHa4w3DID09nezsbAYNGlRlu/z8fCwWC5GRkZW+7nA4KCgoKPcQORMWi4X7kzoz46+9CfK38e2ve7ly2nI25R0yuzQREamEqQFo7969OJ1OoqOjyz0fHR1NTk5Olcfl5+cTGhqK3W5nxIgRTJkyhSFDhlTa9ujRozz22GPceOONVabBtLQ0IiIiPI/Y2Niad0p82vDzWjL37gG0jgxi674irp72HV//XPl0roiImMf0NUA1ERYWRlZWFqtXr+bpp58mNTWVpUuXVmhXUlLC9ddfj2EYTJ8+vcrzjRs3jvz8fM9jxw4tZJWa694qnAX3DqRf+ygOOUq5ZeZqXl+2Ge05KiLScJi6e1uzZs2w2Wzk5uaWez43N5eYmKpvM2C1WomLiwMgPj6ejRs3kpaWxuDBgz1tjoWfbdu28dVXX51yLjAgIICAgICz64zICZqGBjD7tkQmLviR91btIO2zn8nOOcQzf+6hu9CLiDQApo4A2e12+vTpQ3p6uuc5l8tFeno6/fv3r/Z5XC4XDsfx+zcdCz+//vorX375JU2bNq3VukWqw+5n5Zmre/DUledis1qY98PvJP/re3ILjppdmoiIzzN9//7U1FRGjRpFQkIC/fr1Y/LkyRQWFpKSkgLAyJEjad26NWlpaYB7vU5CQgKdOnXC4XCwaNEiZs2a5ZniKikp4dprryUzM5NPP/0Up9PpWU8UFRWF3W43p6PikywWCyP7tyeueShj/l8m/9txkCumLudfNyfQKzbS7PJERHyW6QEoOTmZPXv2MGHCBHJycoiPj2fx4sWehdHbt2/Haj0+UFVYWMiYMWPYuXMnQUFBdO3aldmzZ5OcnAzA77//zoIFCwD39NiJvv7663LTZCL1ZUBcMz65ZyC3zVzDr3mHue71DF64tidXxrc2uzQREZ9k+j5ADZH2AZK6cuhoCQ+8n0V62ZVhdw/uxMNDu2Cz6o7yIiJny2v2ARLxNWGB/vxrZAJjBncCYPrSzdzx7hoOHS0xuTIREd+iACRSz2xWC48O78orN8QT4Gcl/ec8rn7tO7buLTS7NBERn6EAJGKSK+Nb88Gd/YkOD2BT3mGunLaCFZv2ml2WiIhPUAASMVGv2Ej+e++FxMdGkn+khJFvrWLmd1u1aaKISB1TABIxWYvwQN6/4wL+fH5rnC6DiQs28PeP11NcqjvKi4jUFQUgkQYg0N/Gi9f34u+XdsVigfdW7eCvb65k32HH6Q8WEZEzpgAk0kBYLBbuGNSJt0b1JSzAj1Vb93PF1BX8tKvA7NJERBodBSCRBubiri34+J6BdGgWwu8Hj3DN9O/4bP1us8sSEWlUFIBEGqC4FqHMHzOQP3RuxpESJ3f/J5PJX/6Cy6XF0SIitUEBSKSBigj25+3RfbllYAcAJn/5K/e+l0lRcanJlYmIeD8FIJEGzM9mZcLl3Xn+mp742ywsWp/DNdMz2HmgyOzSRES8mgKQiBe4vm8s791+Ac1C7WzcXcCVU1eweut+s8sSEfFaCkAiXiKhfRSf3Hsh3VuGs6+wmL+88T3vr9pudlkiIl5JAUjEi7SODOKju/szokdLSpwGY+et54kFGyh1atNEEZEzoQAk4mWC7X5M/cv5PDTkHADe+W4ro99ezcGiYpMrExHxHgpAIl7IYrFw3yWdmfHXPgTbbSzftJerpq1gU94hs0sTEfEKCkAiXmz4eTHMvXsArSOD2LqviKumfcdXP+eaXZaISIOnACTi5bq1DGfBvQPp1yGKw45Sbp25hhnLNuuO8iIip6AAJNIINA0NYPatifwlsS2GAc9+9jMPzsniaInT7NJERBokBSCRRsLuZ+Xpq85j0pXnYrNamJ+1i+TXM8gtOGp2aSIiDY4CkEgjYrFYuLl/e2bd0o/IYH/+tzOfy6csJ2vHQbNLExFpUBSARBqhAXHNWHDPhZwTHUreIQfXv57Bxz/sNLssEZEGQwFIpJFq2zSYuXcPIKlbC4pLXTw453+kfbYRp+4oLyKiACTSmIUF+vOvmxO45+JOALy+7Dduf3cNh46WmFyZiIi5FIBEGjmr1cIjw7ryyg3xBPhZ+ernPK5+7Tu27i00uzQREdMoAIn4iCvjW/PhXf2JCQ9kU95hrpy2ghWb9ppdloiIKRSARHxIzzaRLLh3IPGxkeQfKWHkW6t4Z8UWbZooIj5HAUjEx7QID+T9Oy7gz71b43QZPPHfnxg3bz3FpbqjvIj4DgUgER8U6G/jxet6Mf7Sblgt8P7qHdz05vfsPewwuzQRkXqhACTioywWC7cP6si/R/clLMCP1VsPcOXUFWzYlW92aSIidU4BSMTHXdylBR/fM5AOzUL4/eARrp2ewWfrd5tdlohInVIAEhHiWoQyf8xA/tC5GUdKnNz9n0xeXvILLm2aKCKNlAKQiAAQEezP26P7cuuFHQB4Jf1X7vl/mRQVl5pcmYhI7VMAEhEPP5uVxy/rzvPX9sRus/LZjzlcMz2DnQeKzC5NRKRWKQCJSAXXJ8Ty3h2JNAsNYOPuAq6cuoJVW/abXZaISK1RABKRSvVpF8WCewdybqtw9hUWc9Ob3/Pequ1mlyUiUitMD0DTpk2jffv2BAYGkpiYyKpVq6psO2/ePBISEoiMjCQkJIT4+HhmzZpVoc3QoUNp2rQpFouFrKysOu6BSOPVKjKIj+4awIieLSlxGoybt56Jn/xIqVObJoqIdzM1AM2ZM4fU1FQmTpxIZmYmvXr1YtiwYeTl5VXaPioqivHjx5ORkcG6detISUkhJSWFzz//3NOmsLCQCy+8kOeee66+uiHSqAXZbUy98XweHnoOADMztjHq7VUcLCo2uTIRkZqzGCbeBCgxMZG+ffsydepUAFwuF7Gxsdx3332MHTu2Wufo3bs3I0aMYNKkSeWe37p1Kx06dOCHH34gPj7+jOoqKCggIiKC/Px8wsPDz+hYkcbs8w05PDgni6JiJ+2aBvPmyAQ6R4eZXZaICHBmv79NGwEqLi5m7dq1JCUlHS/GaiUpKYmMjIzTHm8YBunp6WRnZzNo0KCzqsXhcFBQUFDuISIVDTs3hnljBtCmSRDb9hVx9Wvfkb4x1+yyRETOmGkBaO/evTidTqKjo8s9Hx0dTU5OTpXH5efnExoait1uZ8SIEUyZMoUhQ4acVS1paWlERER4HrGxsWd1PpHGrGtMOAvuvZDEDlEcdpRy27trmL50s+4oLyJexfRF0GcqLCyMrKwsVq9ezdNPP01qaipLly49q3OOGzeO/Px8z2PHjh21U6xIIxUVYmf2bYnclNgWw4DnFv/MA3OyOFriNLs0EZFq8TPrGzdr1gybzUZubvnh89zcXGJiYqo8zmq1EhcXB0B8fDwbN24kLS2NwYMH17iWgIAAAgICany8iC/yt1l5+uoedG0ZzhMLNvBJ1i627i3k9ZsTiIkINLs8EZFTMm0EyG6306dPH9LT0z3PuVwu0tPT6d+/f7XP43K5cDgcdVGiiFTDzRe0Y9at/YgM9ud/O/O5YupysnYcNLssEZFTMnUKLDU1lTfeeIOZM2eyceNG7r77bgoLC0lJSQFg5MiRjBs3ztM+LS2NJUuW8Ntvv7Fx40ZefPFFZs2axV//+ldPm/3795OVlcVPP/0EQHZ2NllZWadcVyQiZ2dAp2YsuOdCukSHkXfIwfWvZ/DxDzvNLktEpEqmTYEBJCcns2fPHiZMmEBOTg7x8fEsXrzYszB6+/btWK3HM1phYSFjxoxh586dBAUF0bVrV2bPnk1ycrKnzYIFCzwBCuCGG24AYOLEiTzxxBP10zERH9S2aTBzxwzggfez+HJjLg/O+R8/7z7Eo8O7YrNazC5PRKQcU/cBaqi0D5BIzblcBi8t+YWpX28C4OIuzXnlxvMJD/Q3uTIRaey8Yh8gEWmcrFYLDw/rwqs3nk+An5Wvs/dw9bQVbNlbaHZpIiIeCkAiUieu6NWKj+4aQEx4IJv3FHLl1OV8++ses8sSEQEUgESkDvVoE8GC+wZyfttICo6WMvrt1by9Yos2TRQR0ykAiUidahEWyHu3X8A1vdvgdBk8+d+fGDt3PY5SbZooIuZRABKROhfob+Of1/Xk/0Z0w2qBOWt2cNMbK9l7WHt4iYg5FIBEpF5YLBZu+0NH3hrdl7BAP9ZsO8AVU5azYVe+2aWJiA9SABKRejW4Swvm3zOQjs1C2JV/lGunZ7Bo/W6zyxIRH6MAJCL1rlPzUD4eM5BB5zTnSImTMf/J5KUlv+ByaXG0iNQPBSARMUVEsD9vjUrgtgs7APBq+q+M+U8mhY5SkysTEV+gACQipvGzWfm/y7rzwrU9sdusLN6QwzXTv2PH/iKzSxORRk4BSERMd11CLO/dkUiz0AB+zjnEldNWsGrLfrPLEpFGTAFIRBqEPu2iWHDvQM5rHc7+wmL+8sb3vLdqu9lliUgjpQAkIg1Gq8ggPrxzAJf1bEmpy2DcvPVM/ORHSpwus0sTkUZGAUhEGpQgu40pN57Pw0PPAWBmxjZGvbWKA4XFJlcmIo2JApCINDgWi4V7/9iZf93chxC7je827+Oq11bwS+4hs0sTkUZCAUhEGqyh58Ywd8wAYqOC2LaviD+/9h1f/pRrdlki0ggoAIlIg9Y1JpxP7rmQCzpGcdhRyu2z1vDa0k26o7yInBUFIBFp8KJC7My6NZG/XtAWw4DnF2fzwJwsjpbojvIiUjMKQCLiFfxtVv5xVQ8mXXUeflYLn2Tt4vrXM8jJP2p2aSLihRSARMSr3HxBO2bdmkiTYH/W7czn8qnL+WH7AbPLEhEvowAkIl6nf6emfHLPhXSJDmPPIQfJ//qeeZk7zS5LRLyIApCIeKW2TYOZO2YAQ7pHU1zqIvWD/5G2aCNO3VFeRKpBAUhEvFZogB+v/7UP914cB8Dr3/zGrTNXU3C0xOTKRKShUwASEa9mtVp4eFgXptx4PoH+VpZm7+GqaSvYsrfQ7NJEpAFTABKRRuHyXq348M4BtIwI5Lc9hVw5dTnf/rrH7LJEpIFSABKRRqNHmwg+uXcgvdtGUnC0lFFvreKt5Vu0aaKIVKAAJCKNSouwQN674wKu7dMGlwFPffoTj81dh6NUmyaKyHEKQCLS6AT42Xjh2p7834huWC3wwZqd3PTGSvYccphdmog0EApAItIoWSwWbvtDR95O6UdYoB9rth3gyqnL+fH3fLNLE5EGwGJocryCgoICIiIiyM/PJzw83OxyROQs/bbnMLfNXMNvewsJ9LfyQNI5tIwIJNDfRrDdRpC/7fjndhvB/n4E2q3YbVYsFovZ5YtINZ3J728FoEooAIk0PvlHSrjvvR/45pfqXxlms1oI8neHoqCygBTof8LndhvBx14/oY37GL+yj1aC/P3cwcrzWtlHfxtWqwKWSG05k9/ffvVUk4iIqSKC/Hl7dF/e+PY31mw9wJGSUo4UOykqdnK0xMmRkuOflzjd/y90ugwOO0o57Cits7oC/KzHR6HsJwcoK8F2v3IjVUEnfDwWyE5+LdgTvmz42ywaxRKphAKQiPgMm9XCXRd1gotO3a7E6eJIiZMjxU5PSPJ8XXLs87IAVeLkaCVtKhxz0sdjHKUuHKUuDlA3u1fbrBaCy8LViaNYFUPTsREtv7JRq+OjWOWOOTmA+WkUS7yTApCIyEn8bVb8bVbCA/3r5Pwul4Gj1EVRcWmloamqQHW0xFl2jMsdwI4d43nt+DGlruOjWIccpRyqp1GsY9OB7nVUFacITw5dlY9o+ZVrp1EsqQsKQCIi9cxqtXhCQV0pcbpOCE3HglQpR4pdZcGptEJoOtWIVvkwVsrREpfne9XnKNaxoBXgbyPQz0qAv40APyuBZR9P/LzCc/5WAvxsBJZ9rLztsXZaAN/YKQCJiDRC/jYrEUFWIoLqbhTraKmz3Dqqyqb9Kgaq0pNGtCqZIiw7zlmPo1iVsftZPSGrstB0cnAK9LdWCGYBpwxmx4858Xl/m3aoqQ8NIgBNmzaNF154gZycHHr16sWUKVPo169fpW3nzZvHM888w6ZNmygpKaFz58489NBD3HzzzZ42hmEwceJE3njjDQ4ePMjAgQOZPn06nTt3rq8uiYg0alarhWC7H8F2P5rW0fcoLnVVMvLkDk2OEhdHS90fHaUujpY4y0ainBwtKf/RUerCcez1E447fry77dFSJydeF11c6qK41AVH6zd42ayWqgOW3/GRrAB/K4EnjFhV2vbk4HWatr60nsv0ADRnzhxSU1OZMWMGiYmJTJ48mWHDhpGdnU2LFi0qtI+KimL8+PF07doVu93Op59+SkpKCi1atGDYsGEAPP/887z66qvMnDmTDh068PjjjzNs2DB++uknAgMD67uLIiJSA3Y/K3a/uhvFOplhGJS6jBPCVFmwqiQsnRysKjvm5LZHq2p7LGiVcboMispGzaijacWq2G3W8iNXVYSsU4atk44JqKJtZLA/YXW0zq46TN8HKDExkb59+zJ16lQAXC4XsbGx3HfffYwdO7Za5+jduzcjRoxg0qRJGIZBq1ateOihh3j44YcByM/PJzo6mnfeeYcbbrjhtOfTPkAiIlKfXC6DYqerQnCqLEB5ni9xcrS04jFVhSzHCR+PnvD1sQXz9e3OQR0Zd2m3Wj2n1+wDVFxczNq1axk3bpznOavVSlJSEhkZGac93jAMvvrqK7Kzs3nuuecA2LJlCzk5OSQlJXnaRUREkJiYSEZGRqUByOFw4HAcv0dQQUHB2XRLRETkjFitFgKt7qvgoH5HRUqdJ08jlg9NRysZ6TqxraOS6cfKpxrLhzh3X81jagDau3cvTqeT6Ojocs9HR0fz888/V3lcfn4+rVu3xuFwYLPZeO211xgyZAgAOTk5nnOcfM5jr50sLS2NJ5988my6IiIi4pX8bFb8bFZCAkxfFVOvvHKpeVhYGFlZWaxevZqnn36a1NRUli5dWuPzjRs3jvz8fM9jx44dtVesiIiINDimxr1mzZphs9nIzc0t93xubi4xMTFVHme1WomLiwMgPj6ejRs3kpaWxuDBgz3H5ebm0rJly3LnjI+Pr/R8AQEBBAQEnGVvRERExFuYOgJkt9vp06cP6enpnudcLhfp6en079+/2udxuVyeNTwdOnQgJiam3DkLCgpYuXLlGZ1TREREGi/TJ/xSU1MZNWoUCQkJ9OvXj8mTJ1NYWEhKSgoAI0eOpHXr1qSlpQHu9ToJCQl06tQJh8PBokWLmDVrFtOnTwfAYrHwwAMP8I9//IPOnTt7LoNv1aoVV111lVndFBERkQbE9ACUnJzMnj17mDBhAjk5OcTHx7N48WLPIubt27djtR4fqCosLGTMmDHs3LmToKAgunbtyuzZs0lOTva0efTRRyksLOSOO+7g4MGDXHjhhSxevFh7AImIiAjQAPYBaoi0D5CIiIj3OZPf3155FZiIiIjI2VAAEhEREZ+jACQiIiI+RwFIREREfI4CkIiIiPgcBSARERHxOQpAIiIi4nMUgERERMTnmL4TdEN0bG/IgoICkysRERGR6jr2e7s6ezwrAFXi0KFDAMTGxppciYiIiJypQ4cOERERcco2uhVGJVwuF7t27SIsLAyLxVKr5y4oKCA2NpYdO3Y0yttsqH/er7H3Uf3zfo29j+pfzRmGwaFDh2jVqlW5+4hWRiNAlbBarbRp06ZOv0d4eHij/It9jPrn/Rp7H9U/79fY+6j+1czpRn6O0SJoERER8TkKQCIiIuJzFIDqWUBAABMnTiQgIMDsUuqE+uf9Gnsf1T/v19j7qP7VDy2CFhEREZ+jESARERHxOQpAIiIi4nMUgERERMTnKACJiIiIz1EAqgPTpk2jffv2BAYGkpiYyKpVq07Z/sMPP6Rr164EBgbSo0cPFi1aVE+V1syZ9O+dd97BYrGUewQGBtZjtWfmm2++4fLLL6dVq1ZYLBbmz59/2mOWLl1K7969CQgIIC4ujnfeeafO66ypM+3f0qVLK7x/FouFnJyc+in4DKWlpdG3b1/CwsJo0aIFV111FdnZ2ac9zlt+BmvSP2/7GZw+fTo9e/b0bJLXv39/Pvvss1Me4y3vH5x5/7zt/TvZs88+i8Vi4YEHHjhlOzPeQwWgWjZnzhxSU1OZOHEimZmZ9OrVi2HDhpGXl1dp+++++44bb7yRW2+9lR9++IGrrrqKq666ih9//LGeK6+eM+0fuHf73L17t+exbdu2eqz4zBQWFtKrVy+mTZtWrfZbtmxhxIgRXHzxxWRlZfHAAw9w22238fnnn9dxpTVzpv07Jjs7u9x72KJFizqq8OwsW7aMe+65h++//54lS5ZQUlLC0KFDKSwsrPIYb/oZrEn/wLt+Btu0acOzzz7L2rVrWbNmDX/84x+58sor2bBhQ6Xtven9gzPvH3jX+3ei1atX8/rrr9OzZ89TtjPtPTSkVvXr18+45557PF87nU6jVatWRlpaWqXtr7/+emPEiBHlnktMTDTuvPPOOq2zps60f2+//bYRERFRT9XVLsD4+OOPT9nm0UcfNc4999xyzyUnJxvDhg2rw8pqR3X69/XXXxuAceDAgXqpqbbl5eUZgLFs2bIq23jbz+CJqtM/b/4ZPKZJkybGm2++Welr3vz+HXOq/nnr+3fo0CGjc+fOxpIlS4yLLrrIuP/++6tsa9Z7qBGgWlRcXMzatWtJSkryPGe1WklKSiIjI6PSYzIyMsq1Bxg2bFiV7c1Uk/4BHD58mHbt2hEbG3va/+l4G296/85GfHw8LVu2ZMiQIaxYscLscqotPz8fgKioqCrbePN7WJ3+gff+DDqdTt5//30KCwvp379/pW28+f2rTv/AO9+/e+65hxEjRlR4bypj1nuoAFSL9u7di9PpJDo6utzz0dHRVa6ZyMnJOaP2ZqpJ/7p06cJbb73FJ598wuzZs3G5XAwYMICdO3fWR8l1rqr3r6CggCNHjphUVe1p2bIlM2bMYO7cucydO5fY2FgGDx5MZmam2aWdlsvl4oEHHmDgwIGcd955Vbbzpp/BE1W3f974M7h+/XpCQ0MJCAjgrrvu4uOPP6Z79+6VtvXG9+9M+ueN79/7779PZmYmaWlp1Wpv1nuou8FLnerfv3+5/9kMGDCAbt268frrrzNp0iQTK5Pq6NKlC126dPF8PWDAADZv3szLL7/MrFmzTKzs9O655x5+/PFHli9fbnYpdaK6/fPGn8EuXbqQlZVFfn4+H330EaNGjWLZsmVVhgRvcyb987b3b8eOHdx///0sWbKkwS/WVgCqRc2aNcNms5Gbm1vu+dzcXGJiYio9JiYm5ozam6km/TuZv78/559/Pps2baqLEutdVe9feHg4QUFBJlVVt/r169fgQ8W9997Lp59+yjfffEObNm1O2dabfgaPOZP+ncwbfgbtdjtxcXEA9OnTh9WrV/PKK6/w+uuvV2jrje/fmfTvZA39/Vu7di15eXn07t3b85zT6eSbb75h6tSpOBwObDZbuWPMeg81BVaL7HY7ffr0IT093fOcy+UiPT29yvnd/v37l2sPsGTJklPOB5ulJv07mdPpZP369bRs2bKuyqxX3vT+1ZasrKwG+/4ZhsG9997Lxx9/zFdffUWHDh1Oe4w3vYc16d/JvPFn0OVy4XA4Kn3Nm96/qpyqfydr6O/fJZdcwvr168nKyvI8EhISuOmmm8jKyqoQfsDE97BOl1j7oPfff98ICAgw3nnnHeOnn34y7rjjDiMyMtLIyckxDMMwbr75ZmPs2LGe9itWrDD8/PyMf/7zn8bGjRuNiRMnGv7+/sb69evN6sIpnWn/nnzySePzzz83Nm/ebKxdu9a44YYbjMDAQGPDhg1mdeGUDh06ZPzwww/GDz/8YADGSy+9ZPzwww/Gtm3bDMMwjLFjxxo333yzp/1vv/1mBAcHG4888oixceNGY9q0aYbNZjMWL15sVhdO6Uz79/LLLxvz5883fv31V2P9+vXG/fffb1itVuPLL780qwundPfddxsRERHG0qVLjd27d3seRUVFnjbe/DNYk/5528/g2LFjjWXLlhlbtmwx1q1bZ4wdO9awWCzGF198YRiGd79/hnHm/fO2968yJ18F1lDeQwWgOjBlyhSjbdu2ht1uN/r162d8//33ntcuuugiY9SoUeXaf/DBB8Y555xj2O1249xzzzUWLlxYzxWfmTPp3wMPPOBpGx0dbVx66aVGZmamCVVXz7HLvk9+HOvTqFGjjIsuuqjCMfHx8Ybdbjc6duxovP322/Ved3Wdaf+ee+45o1OnTkZgYKARFRVlDB482Pjqq6/MKb4aKusbUO498eafwZr0z9t+Bm+55RajXbt2ht1uN5o3b25ccsklnnBgGN79/hnGmffP296/ypwcgBrKe2gxDMOo2zEmERERkYZFa4BERETE5ygAiYiIiM9RABIRERGfowAkIiIiPkcBSERERHyOApCIiIj4HAUgERER8TkKQCIiIuJzFIBERKph6dKlWCwWDh48aHYpIlILFIBERETE5ygAiYiIiM9RABIRr+ByuUhLS6NDhw4EBQXRq1cvPvroI+D49NTChQvp2bMngYGBXHDBBfz444/lzjF37lzOPfdcAgICaN++PS+++GK51x0OB4899hixsbEEBAQQFxfHv//973Jt1q5dS0JCAsHBwQwYMIDs7Oy67biI1AkFIBHxCmlpabz77rvMmDGDDRs28OCDD/LXv/6VZcuWedo88sgjvPjii6xevZrmzZtz+eWXU1JSAriDy/XXX88NN9zA+vXreeKJJ3j88cd55513PMePHDmS9957j1dffZWNGzfy+uuvExoaWq6O8ePH8+KLL7JmzRr8/Py45ZZb6qX/IlK7dDd4EWnwHA4HUVFRfPnll/Tv39/z/G233UZRURF33HEHF198Me+//z7JyckA7N+/nzZt2vDOO+9w/fXXc9NNN7Fnzx6++OILz/GPPvooCxcuZMOGDfzyyy906dKFJUuWkJSUVKGGpUuXcvHFF/Pll19yySWXALBo0SJGjBjBkSNHCAwMrOM/BRGpTRoBEpEGb9OmTRQVFTFkyBBCQ0M9j3fffZfNmzd72p0YjqKioujSpQsbN24EYOPGjQwcOLDceQcOHMivv/6K0+kkKysLm83GRRdddMpaevbs6fm8ZcuWAOTl5Z11H0WkfvmZXYCIyOkcPnwYgIULF9K6detyrwUEBJQLQTUVFBRUrXb+/v6ezy0WC+BenyQi3kUjQCLS4HXv3p2AgAC2b99OXFxcuUdsbKyn3ffff+/5/MCBA/zyyy9069YNgG7durFixYpy512xYgXnnHMONpuNHj164HK5yq0pEpHGSyNAItLghYWF8fDDD/Pggw/icrm48MILyc/PZ8WKFYSHh9OuXTsAnnrqKZo2bUp0dDTjx4+nWbNmXHXVVQA89NBD9O3bl0mTJpGcnExGRgZTp07ltddeA6B9+/aMGjWKW265hVdffZVevXqxbds28vLyuP76683quojUEQUgEfEKkyZNonnz5qSlpfHbb78RGRlJ7969+fvf/+6Zgnr22We5//77+fXXX4mPj+e///0vdrsdgN69e/PBBx8wYcIEJk2aRMuWLXnqqacYPXq053tMnz6dv//974wZM4Z9+/bRtm1b/v73v5vRXRGpY7oKTES83rErtA4cOEBkZKTZ5YiIF9AaIBEREfE5CkAiIiLiczQFJiIiIj5HI0AiIiLicxSARERExOcoAImIiIjPUQASERERn6MAJCIiIj5HAUhERER8jgKQiIiI+BwFIBEREfE5/x8pAB5ljBnfggAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training finished!\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"./revlog_history.tsv\", sep='\\t', index_col=None, dtype={'r_history': str ,'t_history': str} )\n",
    "dataset = dataset[(dataset['i'] > 1) & (dataset['delta_t'] > 0) & (dataset['t_history'].str.count(',0') == 0)]\n",
    "dataset['tensor'] = dataset.progress_apply(lambda x: lineToTensor(list(zip([x['t_history']], [x['r_history']]))[0]), axis=1)\n",
    "dataset['y'] = dataset['r'].map({1: 0, 2: 1, 3: 1, 4: 1})\n",
    "dataset['group'] = dataset['r_history'] + dataset['t_history']\n",
    "print(\"Tensorized!\")\n",
    "\n",
    "from sklearn.model_selection import StratifiedGroupKFold\n",
    "\n",
    "w = []\n",
    "\n",
    "sgkf = StratifiedGroupKFold(n_splits=3)\n",
    "for train_index, test_index in sgkf.split(dataset, dataset['i'], dataset['group']):\n",
    "    print(\"TRAIN:\", len(train_index), \"TEST:\",  len(test_index))\n",
    "    train_set = dataset.iloc[train_index].copy()\n",
    "    test_set = dataset.iloc[test_index].copy()\n",
    "    optimizer = Optimizer(train_set, test_set, n_epoch=3, lr=4e-2, batch_size=256)\n",
    "    w.append(optimizer.train())\n",
    "    optimizer.plot()\n",
    "\n",
    "print(\"\\nTraining finished!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Result\n",
    "\n",
    "Copy the optimal parameters for FSRS for you in the output of next code cell after running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.2378, 2.3546, 4.4158, 1.4006, 1.3607, 0.0092, 1.1812, 0.059, 0.8551, 1.7775, 0.5492, 0.6315, 1.4414]\n"
     ]
    }
   ],
   "source": [
    "w = np.array(w)\n",
    "avg_w = np.round(np.mean(w, axis=0), 4)\n",
    "print(avg_w.tolist())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Preview\n",
    "\n",
    "You can see the memory states and intervals generated by FSRS as if you press the good in each review at the due date scheduled by FSRS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "first rating: 1\n",
      "rating history: 1,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0,1,2,4,9,18,35,67,127,238,439\n",
      "difficulty history: 0,7.2,7.2,7.2,7.1,7.1,7.1,7.1,7.0,7.0,7.0\n",
      "\n",
      "first rating: 2\n",
      "rating history: 2,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0,4,9,21,48,106,229,483,997,2017,4000\n",
      "difficulty history: 0,5.8,5.8,5.8,5.8,5.8,5.8,5.7,5.7,5.7,5.7\n",
      "\n",
      "first rating: 3\n",
      "rating history: 3,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0,6,16,42,107,262,624,1441,3238,7087,15132\n",
      "difficulty history: 0,4.4,4.4,4.4,4.4,4.4,4.4,4.4,4.4,4.4,4.4\n",
      "\n",
      "first rating: 4\n",
      "rating history: 4,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0,8,25,73,204,548,1418,3544,8570,20090,45732\n",
      "difficulty history: 0,3.0,3.0,3.0,3.1,3.1,3.1,3.1,3.1,3.1,3.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "\n",
    "\n",
    "class Collection:\n",
    "    def __init__(self, w):\n",
    "        self.model = FSRS(w)\n",
    "        self.model.eval()\n",
    "\n",
    "    def predict(self, t_history, r_history):\n",
    "        with torch.no_grad():\n",
    "            line_tensor = lineToTensor(list(zip([t_history], [r_history]))[0]).unsqueeze(1)\n",
    "            output_t = self.model(line_tensor)\n",
    "            return output_t[-1][0]\n",
    "        \n",
    "    def batch_predict(self, dataset):\n",
    "        fast_dataset = RevlogDataset(dataset)\n",
    "        outputs, _ = self.model(fast_dataset.x_train.transpose(0, 1))\n",
    "        stabilities, difficulties = outputs[fast_dataset.seq_len-1, torch.arange(len(fast_dataset))].transpose(0, 1)\n",
    "        return stabilities.tolist(), difficulties.tolist()\n",
    "\n",
    "my_collection = Collection(avg_w)\n",
    "print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "for first_rating in (1,2,3,4):\n",
    "    print(f'first rating: {first_rating}')\n",
    "    t_history = \"0\"\n",
    "    d_history = \"0\"\n",
    "    r_history = f\"{first_rating}\"  # the first rating of the new card\n",
    "    # print(\"stability, difficulty, lapses\")\n",
    "    for i in range(10):\n",
    "        states = my_collection.predict(t_history, r_history)\n",
    "        # print('{0:9.2f} {1:11.2f} {2:7.0f}'.format(\n",
    "            # *list(map(lambda x: round(float(x), 4), states))))\n",
    "        next_t = max(1,round(float(9 * states[0] * (1/requestRetention - 1))))\n",
    "        difficulty = round(float(states[1]), 1)\n",
    "        t_history += f',{int(next_t)}'\n",
    "        d_history += f',{difficulty}'\n",
    "        r_history += f\",3\"\n",
    "    print(f\"rating history: {r_history}\")\n",
    "    print(f\"interval history: {t_history}\")\n",
    "    print(f\"difficulty history: {d_history}\")\n",
    "    print('')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can change the `test_rating_sequence` to see the scheduling intervals in different ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([5.9470, 4.4158])\n",
      "tensor([16.2851,  4.4158])\n",
      "tensor([42.3045,  4.4158])\n",
      "tensor([106.8241,   4.4158])\n",
      "tensor([262.3697,   4.4158])\n",
      "tensor([23.5480,  7.1122])\n",
      "tensor([4.3241, 9.7837])\n",
      "tensor([5.6810, 9.7343])\n",
      "tensor([7.7447, 9.6854])\n",
      "tensor([10.5501,  9.6369])\n",
      "tensor([14.4689,  9.5889])\n",
      "tensor([19.5528,  9.5413])\n",
      "rating history: 3,3,3,3,3,1,1,3,3,3,3,3\n",
      "interval history: 0,6,16,42,107,262,24,4,6,8,11,14,20\n",
      "difficulty history: 0,4.4,4.4,4.4,4.4,4.4,7.1,9.8,9.7,9.7,9.6,9.6,9.5\n"
     ]
    }
   ],
   "source": [
    "test_rating_sequence = \"3,3,3,3,3,1,1,3,3,3,3,3\"\n",
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "easyBonus = 1.3\n",
    "hardInterval = 1.2\n",
    "\n",
    "t_history = \"0\"\n",
    "d_history = \"0\"\n",
    "for i in range(len(test_rating_sequence.split(','))):\n",
    "    rating = test_rating_sequence[2*i]\n",
    "    last_t = int(t_history.split(',')[-1])\n",
    "    r_history = test_rating_sequence[:2*i+1]\n",
    "    states = my_collection.predict(t_history, r_history)\n",
    "    print(states)\n",
    "    next_t = max(1,round(float(9 * states[0] * (1/requestRetention - 1))))\n",
    "    if rating == '4':\n",
    "        next_t = round(next_t * easyBonus)\n",
    "    elif rating == '2':\n",
    "        next_t = round(last_t * hardInterval)\n",
    "    t_history += f',{int(next_t)}'\n",
    "    difficulty = round(float(states[1]), 1)\n",
    "    d_history += f',{difficulty}'\n",
    "print(f\"rating history: {test_rating_sequence}\")\n",
    "print(f\"interval history: {t_history}\")\n",
    "print(f\"difficulty history: {d_history}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Predict memory states and distribution of difficulty\n",
    "\n",
    "Predict memory states for each review group and save them in [./prediction.tsv](./prediction.tsv).\n",
    "\n",
    "Meanwhile, it will count the distribution of difficulty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction.tsv saved.\n",
      "difficulty\n",
      "1     0.012619\n",
      "2     0.039576\n",
      "3     0.153002\n",
      "4     0.161755\n",
      "5     0.001233\n",
      "6     0.067128\n",
      "7     0.135807\n",
      "8     0.053978\n",
      "9     0.021100\n",
      "10    0.353801\n",
      "Name: count, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "stabilities = map(lambda x: round(x, 2), stabilities)\n",
    "difficulties = map(lambda x: round(x, 2), difficulties)\n",
    "dataset['stability'] = list(stabilities)\n",
    "dataset['difficulty'] = list(difficulties)\n",
    "prediction = dataset.groupby(by=['t_history', 'r_history']).agg({\"stability\": \"mean\", \"difficulty\": \"mean\", \"id\": \"count\"})\n",
    "prediction.reset_index(inplace=True)\n",
    "prediction.sort_values(by=['r_history'], inplace=True)\n",
    "prediction.rename(columns={\"id\": \"count\"}, inplace=True)\n",
    "prediction.to_csv(\"./prediction.tsv\", sep='\\t', index=None)\n",
    "print(\"prediction.tsv saved.\")\n",
    "prediction['difficulty'] = prediction['difficulty'].map(lambda x: int(round(x)))\n",
    "difficulty_distribution = prediction.groupby(by=['difficulty'])['count'].sum() / prediction['count'].sum()\n",
    "print(difficulty_distribution)\n",
    "difficulty_distribution_padding = np.zeros(10)\n",
    "for i in range(10):\n",
    "    if i+1 in difficulty_distribution.index:\n",
    "        difficulty_distribution_padding[i] = difficulty_distribution.loc[i+1]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Optimize retention to minimize the time of reviews\n",
    "\n",
    "Calculate the optimal retention to minimize the time for long-term memory consolidation. It is an experimental feature. You can use the simulator to get more accurate results:\n",
    "\n",
    "https://github.com/open-spaced-repetition/fsrs4anki/blob/main/fsrs4anki_simulator.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average time for failed cards: 25.0s\n",
      "average time for recalled cards: 8.0s\n",
      "terminal stability:  100.18\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3c1057b6d09f4a4f9b62ac02ed9f16ec",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/15 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "expected_time.csv saved.\n",
      "\n",
      "-----suggested retention (experimental): 0.79-----\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base = 1.01\n",
    "index_len = 664\n",
    "index_offset = 200\n",
    "d_range = 10\n",
    "d_offset = 1\n",
    "r_time = 8\n",
    "f_time = 25\n",
    "max_time = 200000\n",
    "\n",
    "type_block = dict()\n",
    "type_count = dict()\n",
    "type_time = dict()\n",
    "last_t = type_sequence[0]\n",
    "type_block[last_t] = 1\n",
    "type_count[last_t] = 1\n",
    "type_time[last_t] = time_sequence[0]\n",
    "for i,t in enumerate(type_sequence[1:]):\n",
    "    type_count[t] = type_count.setdefault(t, 0) + 1\n",
    "    type_time[t] = type_time.setdefault(t, 0) + time_sequence[i]\n",
    "    if t != last_t:\n",
    "        type_block[t] = type_block.setdefault(t, 0) + 1\n",
    "    last_t = t\n",
    "\n",
    "r_time = round(type_time[1]/type_count[1]/1000, 1)\n",
    "\n",
    "if 2 in type_count and 2 in type_block:\n",
    "    f_time = round(type_time[2]/type_block[2]/1000 + r_time, 1)\n",
    "\n",
    "print(f\"average time for failed cards: {f_time}s\")\n",
    "print(f\"average time for recalled cards: {r_time}s\")\n",
    "\n",
    "def stability2index(stability):\n",
    "    return int(round(np.log(stability) / np.log(base)) + index_offset)\n",
    "\n",
    "def init_stability(d):\n",
    "    return max(((d - avg_w[2]) / -avg_w[3] + 2) * avg_w[1] + avg_w[0], np.power(base, -index_offset))\n",
    "\n",
    "def cal_next_recall_stability(s, r, d, response):\n",
    "    if response == 1:\n",
    "        return s * (1 + np.exp(avg_w[6]) * (11 - d) * np.power(s, -avg_w[7]) * (np.exp((1 - r) * avg_w[8]) - 1))\n",
    "    else:\n",
    "        return avg_w[9] * np.power(d, -avg_w[10]) * np.power(s, avg_w[11]) * np.exp((1 - r) * avg_w[12])\n",
    "\n",
    "\n",
    "stability_list = np.array([np.power(base, i - index_offset) for i in range(index_len)])\n",
    "print(f\"terminal stability: {stability_list.max(): .2f}\")\n",
    "df = pd.DataFrame(columns=[\"retention\", \"difficulty\", \"time\"])\n",
    "\n",
    "for percentage in notebook.tqdm(range(96, 66, -2)):\n",
    "    recall = percentage / 100\n",
    "    time_list = np.zeros((d_range, index_len))\n",
    "    time_list[:,:-1] = max_time\n",
    "    for d in range(d_range, 0, -1):\n",
    "        s0 = init_stability(d)\n",
    "        s0_index = stability2index(s0)\n",
    "        diff = max_time\n",
    "        while diff > 0.1:\n",
    "            s0_time = time_list[d - 1][s0_index]\n",
    "            for s_index in range(index_len - 2, -1, -1):\n",
    "                stability = stability_list[s_index]\n",
    "                interval = max(1, round(stability * np.log(recall) / np.log(0.9)))\n",
    "                p_recall = (1 + interval / (9 * stability)) ** -1\n",
    "                recall_s = cal_next_recall_stability(stability, p_recall, d, 1)\n",
    "                forget_d = min(d + d_offset, 10)\n",
    "                forget_s = cal_next_recall_stability(stability, p_recall, forget_d, 0)\n",
    "                recall_s_index = min(stability2index(recall_s), index_len - 1)\n",
    "                forget_s_index = min(max(stability2index(forget_s), 0), index_len - 1)\n",
    "                recall_time = time_list[d - 1][recall_s_index] + r_time\n",
    "                forget_time = time_list[forget_d - 1][forget_s_index] + f_time\n",
    "                exp_time = p_recall * recall_time + (1.0 - p_recall) * forget_time\n",
    "                if exp_time < time_list[d - 1][s_index]:\n",
    "                    time_list[d - 1][s_index] = exp_time\n",
    "            diff = s0_time - time_list[d - 1][s0_index]\n",
    "        df.loc[0 if pd.isnull(df.index.max()) else df.index.max() + 1] = [recall, d, s0_time]\n",
    "\n",
    "df.sort_values(by=[\"difficulty\", \"retention\"], inplace=True)\n",
    "df.to_csv(\"./expected_time.csv\", index=False)\n",
    "print(\"expected_time.csv saved.\")\n",
    "\n",
    "optimal_retention_list = np.zeros(10)\n",
    "for d in range(1, d_range+1):\n",
    "    retention = df[df[\"difficulty\"] == d][\"retention\"]\n",
    "    time = df[df[\"difficulty\"] == d][\"time\"]\n",
    "    optimal_retention = retention.iat[time.argmin()]\n",
    "    optimal_retention_list[d-1] = optimal_retention\n",
    "    plt.plot(retention, time, label=f\"d={d}, r={optimal_retention}\")\n",
    "print(f\"\\n-----suggested retention (experimental): {np.inner(difficulty_distribution_padding, optimal_retention_list):.2f}-----\")\n",
    "plt.ylabel(\"expected time (second)\")\n",
    "plt.xlabel(\"retention\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.semilogy()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 Evaluate the model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Loss\n",
    "\n",
    "Evaluate the model with the log loss. It will compare the log loss between initial model and trained model.\n",
    "\n",
    "And it will predict the stability, difficulty and retrievability for each revlog in [./evaluation.tsv](./evaluation.tsv)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss before training: 0.3386\n",
      "Loss after training: 0.3149\n"
     ]
    }
   ],
   "source": [
    "my_collection = Collection(init_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = (1 + dataset['delta_t'] / (9 * dataset['stability'])) ** -1\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss before training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "my_collection = Collection(avg_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = (1 + dataset['delta_t'] / (9 * dataset['stability'])) ** -1\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss after training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "tmp = dataset.copy()\n",
    "tmp['stability'] = tmp['stability'].map(lambda x: round(x, 2))\n",
    "tmp['difficulty'] = tmp['difficulty'].map(lambda x: round(x, 2))\n",
    "tmp['p'] = tmp['p'].map(lambda x: round(x, 2))\n",
    "tmp['log_loss'] = tmp['log_loss'].map(lambda x: round(x, 2))\n",
    "tmp.rename(columns={\"r\": \"grade\", \"p\": \"retrievability\"}, inplace=True)\n",
    "tmp[['id', 'cid', 'review_date', 'r_history', 't_history', 'delta_t', 'grade', 'stability', 'difficulty', 'retrievability', 'log_loss']].to_csv(\"./evaluation.tsv\", sep='\\t', index=False)\n",
    "del tmp"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Calibration graph\n",
    "\n",
    "1. FSRS predicts the stability and retention for each review.\n",
    "2. Reviews are grouped into 40 bins according to their predicted retention.\n",
    "3. Count the true retention of each bin.\n",
    "4. Plot (predicted retention, true retention) in the line graph.\n",
    "5. Plot (predicted retention, size of bin) in the bar graph.\n",
    "6. Combine these graphs to create the calibration graph.\n",
    "\n",
    "Ideally, the blue line should be aligned with the orange one. If the blue line is higher than the orange line, the FSRS underestimates the retention. When the size of reviews within a bin is small, actual retention may deviate largely, which is normal.\n",
    "\n",
    "R-squared (aka the coefficient of determination), is the proportion of the variation in the dependent variable that is predictable from the independent variable(s). The higher the R-squared, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Coefficient_of_determination\n",
    "\n",
    "RMSE (root mean squared error) is the square root of the average of squared differences between prediction and actual observation. The lower the RMSE, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Root-mean-square_deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.9445\n",
      "RMSE: 0.0151\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "\n",
    "# code from https://github.com/papousek/duolingo-halflife-regression/blob/master/evaluation.py\n",
    "def load_brier(predictions, real, bins=20):\n",
    "    counts = np.zeros(bins)\n",
    "    correct = np.zeros(bins)\n",
    "    prediction = np.zeros(bins)\n",
    "    for p, r in zip(predictions, real):\n",
    "        bin = min(int(p * bins), bins - 1)\n",
    "        counts[bin] += 1\n",
    "        correct[bin] += r\n",
    "        prediction[bin] += p\n",
    "    np.seterr(invalid='ignore')\n",
    "    prediction_means = prediction / counts\n",
    "    prediction_means[np.isnan(prediction_means)] = ((np.arange(bins) + 0.5) / bins)[np.isnan(prediction_means)]\n",
    "    correct_means = correct / counts\n",
    "    correct_means[np.isnan(correct_means)] = 0\n",
    "    size = len(predictions)\n",
    "    answer_mean = sum(correct) / size\n",
    "    return {\n",
    "        \"reliability\": sum(counts * (correct_means - prediction_means) ** 2) / size,\n",
    "        \"resolution\": sum(counts * (correct_means - answer_mean) ** 2) / size,\n",
    "        \"uncertainty\": answer_mean * (1 - answer_mean),\n",
    "        \"detail\": {\n",
    "            \"bin_count\": bins,\n",
    "            \"bin_counts\": list(counts),\n",
    "            \"bin_prediction_means\": list(prediction_means),\n",
    "            \"bin_correct_means\": list(correct_means),\n",
    "        }\n",
    "    }\n",
    "\n",
    "\n",
    "def plot_brier(predictions, real, bins=20):\n",
    "    brier = load_brier(predictions, real, bins=bins)\n",
    "    bin_prediction_means = brier['detail']['bin_prediction_means']\n",
    "    bin_correct_means = brier['detail']['bin_correct_means']\n",
    "    bin_counts = brier['detail']['bin_counts']\n",
    "    r2 = r2_score(bin_correct_means, bin_prediction_means, sample_weight=bin_counts)\n",
    "    rmse = np.sqrt(mean_squared_error(bin_correct_means, bin_prediction_means, sample_weight=bin_counts))\n",
    "    print(f\"R-squared: {r2:.4f}\")\n",
    "    print(f\"RMSE: {rmse:.4f}\")\n",
    "    plt.figure()\n",
    "    ax = plt.gca()\n",
    "    ax.set_xlim([0, 1])\n",
    "    ax.set_ylim([0, 1])\n",
    "    plt.grid(True)\n",
    "    plt.plot(bin_prediction_means, bin_correct_means, label='Actual Calibration')\n",
    "    plt.plot((0, 1), (0, 1), label='Perfect Calibration')\n",
    "    bin_count = brier['detail']['bin_count']\n",
    "    counts = np.array(bin_counts)\n",
    "    bins = (np.arange(bin_count) + 0.5) / bin_count\n",
    "    plt.legend(loc='upper center')\n",
    "    plt.xlabel('Predicted R')\n",
    "    plt.ylabel('Actual R')\n",
    "    plt.twinx()\n",
    "    plt.ylabel('Number of reviews')\n",
    "    plt.bar(bins, counts, width=(0.8 / bin_count), ec='k', lw=.2, alpha=0.5, label='Number of reviews')\n",
    "    plt.legend(loc='lower center')\n",
    "\n",
    "\n",
    "plot_brier(dataset['p'], dataset['y'], bins=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as ticker\n",
    "\n",
    "def to_percent(temp, position):\n",
    "    return '%1.0f' % (100 * temp) + '%'\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "stability_calibration = pd.DataFrame(columns=['stability', 'predicted_retention', 'actual_retention'])\n",
    "stability_calibration = dataset[['stability', 'p', 'y']].copy()\n",
    "stability_calibration['bin'] = stability_calibration['stability'].map(lambda x: math.pow(1.2, math.floor(math.log(x, 1.2))))\n",
    "stability_group = stability_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=stability_group.index, height=stability_group['y'], width=stability_group.index / 5.5,\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Stability (days)\")\n",
    "ax1.semilogx()\n",
    "lns.append(lns1)\n",
    "\n",
    "stability_group = stability_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(stability_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(stability_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  background-color: #ffa9a9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row6_col6, #T_5de79_row12_col9, #T_5de79_row16_col7 {\n",
       "  background-color: #f9f9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row6_col7, #T_5de79_row6_col9, #T_5de79_row7_col5, #T_5de79_row8_col8, #T_5de79_row9_col4 {\n",
       "  background-color: #e5e5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row6_col8, #T_5de79_row8_col9, #T_5de79_row9_col7, #T_5de79_row17_col2 {\n",
       "  background-color: #ffe9e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row7_col4 {\n",
       "  background-color: #8585ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row7_col6 {\n",
       "  background-color: #bdbdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row7_col7, #T_5de79_row14_col4 {\n",
       "  background-color: #e1e1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row7_col8, #T_5de79_row15_col5, #T_5de79_row21_col5 {\n",
       "  background-color: #ffbdbd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row7_col9, #T_5de79_row10_col8, #T_5de79_row18_col3 {\n",
       "  background-color: #f5f5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row8_col2, #T_5de79_row11_col0, #T_5de79_row19_col0 {\n",
       "  background-color: #6161ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row8_col3, #T_5de79_row10_col9, #T_5de79_row13_col3, #T_5de79_row15_col3 {\n",
       "  background-color: #fff5f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row8_col4, #T_5de79_row20_col7, #T_5de79_row21_col7 {\n",
       "  background-color: #800000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row8_col5, #T_5de79_row9_col8, #T_5de79_row12_col4, #T_5de79_row16_col4, #T_5de79_row17_col7, #T_5de79_row23_col2 {\n",
       "  background-color: #fdfdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row8_col6 {\n",
       "  background-color: #ddddff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row8_col7, #T_5de79_row10_col5, #T_5de79_row11_col9 {\n",
       "  background-color: #ffeded;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row9_col2, #T_5de79_row9_col3, #T_5de79_row15_col2, #T_5de79_row15_col8 {\n",
       "  background-color: #d9d9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row9_col6, #T_5de79_row12_col8 {\n",
       "  background-color: #ededff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row10_col1, #T_5de79_row11_col8, #T_5de79_row16_col2, #T_5de79_row23_col0 {\n",
       "  background-color: #f1f1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row10_col3, #T_5de79_row15_col1 {\n",
       "  background-color: #c5c5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row10_col7 {\n",
       "  background-color: #ffcdcd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row11_col1, #T_5de79_row21_col0 {\n",
       "  background-color: #9595ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row11_col6, #T_5de79_row12_col5, #T_5de79_row19_col6 {\n",
       "  background-color: #ffd1d1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row11_col7, #T_5de79_row19_col4 {\n",
       "  background-color: #ffd5d5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row12_col0 {\n",
       "  background-color: #6565ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row12_col1, #T_5de79_row19_col1 {\n",
       "  background-color: #a1a1ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row12_col2 {\n",
       "  background-color: #b9b9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row12_col3 {\n",
       "  background-color: #e9e9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row12_col6 {\n",
       "  background-color: #ff9d9d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row12_col7, #T_5de79_row13_col6 {\n",
       "  background-color: #ffa5a5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row13_col0 {\n",
       "  background-color: #7979ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row13_col1 {\n",
       "  background-color: #a5a5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row13_col5, #T_5de79_row18_col5 {\n",
       "  background-color: #ffc9c9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row13_col8, #T_5de79_row19_col7 {\n",
       "  background-color: #ff5151;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row13_col9, #T_5de79_row21_col2 {\n",
       "  background-color: #fff1f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row14_col0, #T_5de79_row16_col0, #T_5de79_row21_col6 {\n",
       "  background-color: #8989ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row14_col1, #T_5de79_row20_col3 {\n",
       "  background-color: #b1b1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row14_col3, #T_5de79_row16_col3, #T_5de79_row17_col3, #T_5de79_row19_col2 {\n",
       "  background-color: #fffdfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row14_col5, #T_5de79_row17_col5, #T_5de79_row20_col0, #T_5de79_row20_col4 {\n",
       "  background-color: #ff9999;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row14_col8 {\n",
       "  background-color: #4141ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row15_col4 {\n",
       "  background-color: #7171ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row15_col6, #T_5de79_row15_col9 {\n",
       "  background-color: #ffc5c5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row15_col7 {\n",
       "  background-color: #ff8989;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row16_col5, #T_5de79_row20_col6 {\n",
       "  background-color: #ff7575;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row16_col6 {\n",
       "  background-color: #ffb9b9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row16_col8 {\n",
       "  background-color: #0000e1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row17_col0, #T_5de79_row18_col8 {\n",
       "  background-color: #6d6dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row17_col4 {\n",
       "  background-color: #5151ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row17_col6 {\n",
       "  background-color: #ffdddd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row17_col8 {\n",
       "  background-color: #0101ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row17_col9 {\n",
       "  background-color: #ff8d8d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row18_col0 {\n",
       "  background-color: #4d4dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row18_col2 {\n",
       "  background-color: #c1c1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row18_col4 {\n",
       "  background-color: #d00000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row18_col6, #T_5de79_row22_col2 {\n",
       "  background-color: #ffadad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row18_col7 {\n",
       "  background-color: #e80000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row18_col9 {\n",
       "  background-color: #8e0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row19_col5 {\n",
       "  background-color: #ff1d1d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row19_col8 {\n",
       "  background-color: #0d0dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row19_col9 {\n",
       "  background-color: #9191ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row20_col2 {\n",
       "  background-color: #cdcdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5de79_row20_col5 {\n",
       "  background-color: #ff4d4d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row21_col1 {\n",
       "  background-color: #ff5959;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row21_col3 {\n",
       "  background-color: #ff5555;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row22_col1 {\n",
       "  background-color: #860000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5de79_row22_col3 {\n",
       "  background-color: #ff4545;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_5de79_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >d_bin</th>\n",
       "      <th class=\"col_heading level0 col0\" >1</th>\n",
       "      <th class=\"col_heading level0 col1\" >2</th>\n",
       "      <th class=\"col_heading level0 col2\" >3</th>\n",
       "      <th class=\"col_heading level0 col3\" >4</th>\n",
       "      <th class=\"col_heading level0 col4\" >5</th>\n",
       "      <th class=\"col_heading level0 col5\" >6</th>\n",
       "      <th class=\"col_heading level0 col6\" >7</th>\n",
       "      <th class=\"col_heading level0 col7\" >8</th>\n",
       "      <th class=\"col_heading level0 col8\" >9</th>\n",
       "      <th class=\"col_heading level0 col9\" >10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >s_bin</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row0\" class=\"row_heading level0 row0\" >0.36</th>\n",
       "      <td id=\"T_5de79_row0_col0\" class=\"data row0 col0\" ></td>\n",
       "      <td id=\"T_5de79_row0_col1\" class=\"data row0 col1\" ></td>\n",
       "      <td id=\"T_5de79_row0_col2\" class=\"data row0 col2\" ></td>\n",
       "      <td id=\"T_5de79_row0_col3\" class=\"data row0 col3\" ></td>\n",
       "      <td id=\"T_5de79_row0_col4\" class=\"data row0 col4\" ></td>\n",
       "      <td id=\"T_5de79_row0_col5\" class=\"data row0 col5\" ></td>\n",
       "      <td id=\"T_5de79_row0_col6\" class=\"data row0 col6\" ></td>\n",
       "      <td id=\"T_5de79_row0_col7\" class=\"data row0 col7\" ></td>\n",
       "      <td id=\"T_5de79_row0_col8\" class=\"data row0 col8\" ></td>\n",
       "      <td id=\"T_5de79_row0_col9\" class=\"data row0 col9\" >3.62%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row1\" class=\"row_heading level0 row1\" >0.51</th>\n",
       "      <td id=\"T_5de79_row1_col0\" class=\"data row1 col0\" ></td>\n",
       "      <td id=\"T_5de79_row1_col1\" class=\"data row1 col1\" ></td>\n",
       "      <td id=\"T_5de79_row1_col2\" class=\"data row1 col2\" ></td>\n",
       "      <td id=\"T_5de79_row1_col3\" class=\"data row1 col3\" ></td>\n",
       "      <td id=\"T_5de79_row1_col4\" class=\"data row1 col4\" ></td>\n",
       "      <td id=\"T_5de79_row1_col5\" class=\"data row1 col5\" ></td>\n",
       "      <td id=\"T_5de79_row1_col6\" class=\"data row1 col6\" ></td>\n",
       "      <td id=\"T_5de79_row1_col7\" class=\"data row1 col7\" ></td>\n",
       "      <td id=\"T_5de79_row1_col8\" class=\"data row1 col8\" ></td>\n",
       "      <td id=\"T_5de79_row1_col9\" class=\"data row1 col9\" >5.06%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row2\" class=\"row_heading level0 row2\" >0.71</th>\n",
       "      <td id=\"T_5de79_row2_col0\" class=\"data row2 col0\" ></td>\n",
       "      <td id=\"T_5de79_row2_col1\" class=\"data row2 col1\" ></td>\n",
       "      <td id=\"T_5de79_row2_col2\" class=\"data row2 col2\" ></td>\n",
       "      <td id=\"T_5de79_row2_col3\" class=\"data row2 col3\" ></td>\n",
       "      <td id=\"T_5de79_row2_col4\" class=\"data row2 col4\" ></td>\n",
       "      <td id=\"T_5de79_row2_col5\" class=\"data row2 col5\" ></td>\n",
       "      <td id=\"T_5de79_row2_col6\" class=\"data row2 col6\" ></td>\n",
       "      <td id=\"T_5de79_row2_col7\" class=\"data row2 col7\" ></td>\n",
       "      <td id=\"T_5de79_row2_col8\" class=\"data row2 col8\" ></td>\n",
       "      <td id=\"T_5de79_row2_col9\" class=\"data row2 col9\" >1.22%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row3\" class=\"row_heading level0 row3\" >1.0</th>\n",
       "      <td id=\"T_5de79_row3_col0\" class=\"data row3 col0\" ></td>\n",
       "      <td id=\"T_5de79_row3_col1\" class=\"data row3 col1\" ></td>\n",
       "      <td id=\"T_5de79_row3_col2\" class=\"data row3 col2\" ></td>\n",
       "      <td id=\"T_5de79_row3_col3\" class=\"data row3 col3\" ></td>\n",
       "      <td id=\"T_5de79_row3_col4\" class=\"data row3 col4\" ></td>\n",
       "      <td id=\"T_5de79_row3_col5\" class=\"data row3 col5\" ></td>\n",
       "      <td id=\"T_5de79_row3_col6\" class=\"data row3 col6\" >6.21%</td>\n",
       "      <td id=\"T_5de79_row3_col7\" class=\"data row3 col7\" >6.27%</td>\n",
       "      <td id=\"T_5de79_row3_col8\" class=\"data row3 col8\" >-2.04%</td>\n",
       "      <td id=\"T_5de79_row3_col9\" class=\"data row3 col9\" >-1.70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row4\" class=\"row_heading level0 row4\" >1.4</th>\n",
       "      <td id=\"T_5de79_row4_col0\" class=\"data row4 col0\" ></td>\n",
       "      <td id=\"T_5de79_row4_col1\" class=\"data row4 col1\" ></td>\n",
       "      <td id=\"T_5de79_row4_col2\" class=\"data row4 col2\" ></td>\n",
       "      <td id=\"T_5de79_row4_col3\" class=\"data row4 col3\" ></td>\n",
       "      <td id=\"T_5de79_row4_col4\" class=\"data row4 col4\" ></td>\n",
       "      <td id=\"T_5de79_row4_col5\" class=\"data row4 col5\" ></td>\n",
       "      <td id=\"T_5de79_row4_col6\" class=\"data row4 col6\" >2.43%</td>\n",
       "      <td id=\"T_5de79_row4_col7\" class=\"data row4 col7\" >0.29%</td>\n",
       "      <td id=\"T_5de79_row4_col8\" class=\"data row4 col8\" >7.87%</td>\n",
       "      <td id=\"T_5de79_row4_col9\" class=\"data row4 col9\" >-2.05%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row5\" class=\"row_heading level0 row5\" >1.96</th>\n",
       "      <td id=\"T_5de79_row5_col0\" class=\"data row5 col0\" ></td>\n",
       "      <td id=\"T_5de79_row5_col1\" class=\"data row5 col1\" ></td>\n",
       "      <td id=\"T_5de79_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "      <td id=\"T_5de79_row5_col3\" class=\"data row5 col3\" ></td>\n",
       "      <td id=\"T_5de79_row5_col4\" class=\"data row5 col4\" ></td>\n",
       "      <td id=\"T_5de79_row5_col5\" class=\"data row5 col5\" >8.29%</td>\n",
       "      <td id=\"T_5de79_row5_col6\" class=\"data row5 col6\" >0.96%</td>\n",
       "      <td id=\"T_5de79_row5_col7\" class=\"data row5 col7\" >-3.17%</td>\n",
       "      <td id=\"T_5de79_row5_col8\" class=\"data row5 col8\" >1.41%</td>\n",
       "      <td id=\"T_5de79_row5_col9\" class=\"data row5 col9\" >-1.78%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row6\" class=\"row_heading level0 row6\" >2.74</th>\n",
       "      <td id=\"T_5de79_row6_col0\" class=\"data row6 col0\" ></td>\n",
       "      <td id=\"T_5de79_row6_col1\" class=\"data row6 col1\" ></td>\n",
       "      <td id=\"T_5de79_row6_col2\" class=\"data row6 col2\" ></td>\n",
       "      <td id=\"T_5de79_row6_col3\" class=\"data row6 col3\" >-2.96%</td>\n",
       "      <td id=\"T_5de79_row6_col4\" class=\"data row6 col4\" ></td>\n",
       "      <td id=\"T_5de79_row6_col5\" class=\"data row6 col5\" >3.43%</td>\n",
       "      <td id=\"T_5de79_row6_col6\" class=\"data row6 col6\" >-0.27%</td>\n",
       "      <td id=\"T_5de79_row6_col7\" class=\"data row6 col7\" >-1.02%</td>\n",
       "      <td id=\"T_5de79_row6_col8\" class=\"data row6 col8\" >0.80%</td>\n",
       "      <td id=\"T_5de79_row6_col9\" class=\"data row6 col9\" >-1.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row7\" class=\"row_heading level0 row7\" >3.84</th>\n",
       "      <td id=\"T_5de79_row7_col0\" class=\"data row7 col0\" ></td>\n",
       "      <td id=\"T_5de79_row7_col1\" class=\"data row7 col1\" ></td>\n",
       "      <td id=\"T_5de79_row7_col2\" class=\"data row7 col2\" ></td>\n",
       "      <td id=\"T_5de79_row7_col3\" class=\"data row7 col3\" >-1.82%</td>\n",
       "      <td id=\"T_5de79_row7_col4\" class=\"data row7 col4\" >-4.83%</td>\n",
       "      <td id=\"T_5de79_row7_col5\" class=\"data row7 col5\" >-0.96%</td>\n",
       "      <td id=\"T_5de79_row7_col6\" class=\"data row7 col6\" >-2.50%</td>\n",
       "      <td id=\"T_5de79_row7_col7\" class=\"data row7 col7\" >-1.10%</td>\n",
       "      <td id=\"T_5de79_row7_col8\" class=\"data row7 col8\" >2.50%</td>\n",
       "      <td id=\"T_5de79_row7_col9\" class=\"data row7 col9\" >-0.46%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row8\" class=\"row_heading level0 row8\" >5.38</th>\n",
       "      <td id=\"T_5de79_row8_col0\" class=\"data row8 col0\" ></td>\n",
       "      <td id=\"T_5de79_row8_col1\" class=\"data row8 col1\" ></td>\n",
       "      <td id=\"T_5de79_row8_col2\" class=\"data row8 col2\" >-6.14%</td>\n",
       "      <td id=\"T_5de79_row8_col3\" class=\"data row8 col3\" >0.36%</td>\n",
       "      <td id=\"T_5de79_row8_col4\" class=\"data row8 col4\" >42.91%</td>\n",
       "      <td id=\"T_5de79_row8_col5\" class=\"data row8 col5\" >-0.04%</td>\n",
       "      <td id=\"T_5de79_row8_col6\" class=\"data row8 col6\" >-1.38%</td>\n",
       "      <td id=\"T_5de79_row8_col7\" class=\"data row8 col7\" >0.69%</td>\n",
       "      <td id=\"T_5de79_row8_col8\" class=\"data row8 col8\" >-0.96%</td>\n",
       "      <td id=\"T_5de79_row8_col9\" class=\"data row8 col9\" >0.87%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row9\" class=\"row_heading level0 row9\" >7.53</th>\n",
       "      <td id=\"T_5de79_row9_col0\" class=\"data row9 col0\" ></td>\n",
       "      <td id=\"T_5de79_row9_col1\" class=\"data row9 col1\" ></td>\n",
       "      <td id=\"T_5de79_row9_col2\" class=\"data row9 col2\" >-1.44%</td>\n",
       "      <td id=\"T_5de79_row9_col3\" class=\"data row9 col3\" >-1.48%</td>\n",
       "      <td id=\"T_5de79_row9_col4\" class=\"data row9 col4\" >-1.06%</td>\n",
       "      <td id=\"T_5de79_row9_col5\" class=\"data row9 col5\" >0.99%</td>\n",
       "      <td id=\"T_5de79_row9_col6\" class=\"data row9 col6\" >-0.72%</td>\n",
       "      <td id=\"T_5de79_row9_col7\" class=\"data row9 col7\" >0.81%</td>\n",
       "      <td id=\"T_5de79_row9_col8\" class=\"data row9 col8\" >-0.06%</td>\n",
       "      <td id=\"T_5de79_row9_col9\" class=\"data row9 col9\" >0.19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row10\" class=\"row_heading level0 row10\" >10.54</th>\n",
       "      <td id=\"T_5de79_row10_col0\" class=\"data row10 col0\" ></td>\n",
       "      <td id=\"T_5de79_row10_col1\" class=\"data row10 col1\" >-0.51%</td>\n",
       "      <td id=\"T_5de79_row10_col2\" class=\"data row10 col2\" >-2.85%</td>\n",
       "      <td id=\"T_5de79_row10_col3\" class=\"data row10 col3\" >-2.31%</td>\n",
       "      <td id=\"T_5de79_row10_col4\" class=\"data row10 col4\" >-2.93%</td>\n",
       "      <td id=\"T_5de79_row10_col5\" class=\"data row10 col5\" >0.63%</td>\n",
       "      <td id=\"T_5de79_row10_col6\" class=\"data row10 col6\" >1.41%</td>\n",
       "      <td id=\"T_5de79_row10_col7\" class=\"data row10 col7\" >2.00%</td>\n",
       "      <td id=\"T_5de79_row10_col8\" class=\"data row10 col8\" >-0.41%</td>\n",
       "      <td id=\"T_5de79_row10_col9\" class=\"data row10 col9\" >0.43%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row11\" class=\"row_heading level0 row11\" >14.76</th>\n",
       "      <td id=\"T_5de79_row11_col0\" class=\"data row11 col0\" >-6.17%</td>\n",
       "      <td id=\"T_5de79_row11_col1\" class=\"data row11 col1\" >-4.08%</td>\n",
       "      <td id=\"T_5de79_row11_col2\" class=\"data row11 col2\" >-2.91%</td>\n",
       "      <td id=\"T_5de79_row11_col3\" class=\"data row11 col3\" >0.23%</td>\n",
       "      <td id=\"T_5de79_row11_col4\" class=\"data row11 col4\" >6.20%</td>\n",
       "      <td id=\"T_5de79_row11_col5\" class=\"data row11 col5\" >1.19%</td>\n",
       "      <td id=\"T_5de79_row11_col6\" class=\"data row11 col6\" >1.86%</td>\n",
       "      <td id=\"T_5de79_row11_col7\" class=\"data row11 col7\" >1.61%</td>\n",
       "      <td id=\"T_5de79_row11_col8\" class=\"data row11 col8\" >-0.48%</td>\n",
       "      <td id=\"T_5de79_row11_col9\" class=\"data row11 col9\" >0.68%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row12\" class=\"row_heading level0 row12\" >20.66</th>\n",
       "      <td id=\"T_5de79_row12_col0\" class=\"data row12 col0\" >-5.99%</td>\n",
       "      <td id=\"T_5de79_row12_col1\" class=\"data row12 col1\" >-3.73%</td>\n",
       "      <td id=\"T_5de79_row12_col2\" class=\"data row12 col2\" >-2.69%</td>\n",
       "      <td id=\"T_5de79_row12_col3\" class=\"data row12 col3\" >-0.81%</td>\n",
       "      <td id=\"T_5de79_row12_col4\" class=\"data row12 col4\" >-0.16%</td>\n",
       "      <td id=\"T_5de79_row12_col5\" class=\"data row12 col5\" >1.74%</td>\n",
       "      <td id=\"T_5de79_row12_col6\" class=\"data row12 col6\" >3.75%</td>\n",
       "      <td id=\"T_5de79_row12_col7\" class=\"data row12 col7\" >3.53%</td>\n",
       "      <td id=\"T_5de79_row12_col8\" class=\"data row12 col8\" >-0.75%</td>\n",
       "      <td id=\"T_5de79_row12_col9\" class=\"data row12 col9\" >-0.20%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row13\" class=\"row_heading level0 row13\" >28.93</th>\n",
       "      <td id=\"T_5de79_row13_col0\" class=\"data row13 col0\" >-5.30%</td>\n",
       "      <td id=\"T_5de79_row13_col1\" class=\"data row13 col1\" >-3.48%</td>\n",
       "      <td id=\"T_5de79_row13_col2\" class=\"data row13 col2\" >-1.75%</td>\n",
       "      <td id=\"T_5de79_row13_col3\" class=\"data row13 col3\" >0.42%</td>\n",
       "      <td id=\"T_5de79_row13_col4\" class=\"data row13 col4\" >-1.58%</td>\n",
       "      <td id=\"T_5de79_row13_col5\" class=\"data row13 col5\" >2.10%</td>\n",
       "      <td id=\"T_5de79_row13_col6\" class=\"data row13 col6\" >3.49%</td>\n",
       "      <td id=\"T_5de79_row13_col7\" class=\"data row13 col7\" >1.50%</td>\n",
       "      <td id=\"T_5de79_row13_col8\" class=\"data row13 col8\" >6.76%</td>\n",
       "      <td id=\"T_5de79_row13_col9\" class=\"data row13 col9\" >0.61%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row14\" class=\"row_heading level0 row14\" >40.5</th>\n",
       "      <td id=\"T_5de79_row14_col0\" class=\"data row14 col0\" >-4.57%</td>\n",
       "      <td id=\"T_5de79_row14_col1\" class=\"data row14 col1\" >-3.02%</td>\n",
       "      <td id=\"T_5de79_row14_col2\" class=\"data row14 col2\" >-2.10%</td>\n",
       "      <td id=\"T_5de79_row14_col3\" class=\"data row14 col3\" >0.05%</td>\n",
       "      <td id=\"T_5de79_row14_col4\" class=\"data row14 col4\" >-1.21%</td>\n",
       "      <td id=\"T_5de79_row14_col5\" class=\"data row14 col5\" >4.01%</td>\n",
       "      <td id=\"T_5de79_row14_col6\" class=\"data row14 col6\" >1.20%</td>\n",
       "      <td id=\"T_5de79_row14_col7\" class=\"data row14 col7\" >3.73%</td>\n",
       "      <td id=\"T_5de79_row14_col8\" class=\"data row14 col8\" >-7.46%</td>\n",
       "      <td id=\"T_5de79_row14_col9\" class=\"data row14 col9\" >3.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row15\" class=\"row_heading level0 row15\" >56.69</th>\n",
       "      <td id=\"T_5de79_row15_col0\" class=\"data row15 col0\" >-1.74%</td>\n",
       "      <td id=\"T_5de79_row15_col1\" class=\"data row15 col1\" >-2.24%</td>\n",
       "      <td id=\"T_5de79_row15_col2\" class=\"data row15 col2\" >-1.45%</td>\n",
       "      <td id=\"T_5de79_row15_col3\" class=\"data row15 col3\" >0.38%</td>\n",
       "      <td id=\"T_5de79_row15_col4\" class=\"data row15 col4\" >-5.59%</td>\n",
       "      <td id=\"T_5de79_row15_col5\" class=\"data row15 col5\" >2.61%</td>\n",
       "      <td id=\"T_5de79_row15_col6\" class=\"data row15 col6\" >2.19%</td>\n",
       "      <td id=\"T_5de79_row15_col7\" class=\"data row15 col7\" >4.59%</td>\n",
       "      <td id=\"T_5de79_row15_col8\" class=\"data row15 col8\" >-1.49%</td>\n",
       "      <td id=\"T_5de79_row15_col9\" class=\"data row15 col9\" >2.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row16\" class=\"row_heading level0 row16\" >79.37</th>\n",
       "      <td id=\"T_5de79_row16_col0\" class=\"data row16 col0\" >-4.61%</td>\n",
       "      <td id=\"T_5de79_row16_col1\" class=\"data row16 col1\" >-1.75%</td>\n",
       "      <td id=\"T_5de79_row16_col2\" class=\"data row16 col2\" >-0.56%</td>\n",
       "      <td id=\"T_5de79_row16_col3\" class=\"data row16 col3\" >0.05%</td>\n",
       "      <td id=\"T_5de79_row16_col4\" class=\"data row16 col4\" >-0.09%</td>\n",
       "      <td id=\"T_5de79_row16_col5\" class=\"data row16 col5\" >5.38%</td>\n",
       "      <td id=\"T_5de79_row16_col6\" class=\"data row16 col6\" >2.70%</td>\n",
       "      <td id=\"T_5de79_row16_col7\" class=\"data row16 col7\" >-0.20%</td>\n",
       "      <td id=\"T_5de79_row16_col8\" class=\"data row16 col8\" >-11.68%</td>\n",
       "      <td id=\"T_5de79_row16_col9\" class=\"data row16 col9\" >2.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row17\" class=\"row_heading level0 row17\" >111.12</th>\n",
       "      <td id=\"T_5de79_row17_col0\" class=\"data row17 col0\" >-5.67%</td>\n",
       "      <td id=\"T_5de79_row17_col1\" class=\"data row17 col1\" >-1.74%</td>\n",
       "      <td id=\"T_5de79_row17_col2\" class=\"data row17 col2\" >0.89%</td>\n",
       "      <td id=\"T_5de79_row17_col3\" class=\"data row17 col3\" >0.00%</td>\n",
       "      <td id=\"T_5de79_row17_col4\" class=\"data row17 col4\" >-6.75%</td>\n",
       "      <td id=\"T_5de79_row17_col5\" class=\"data row17 col5\" >3.92%</td>\n",
       "      <td id=\"T_5de79_row17_col6\" class=\"data row17 col6\" >1.31%</td>\n",
       "      <td id=\"T_5de79_row17_col7\" class=\"data row17 col7\" >-0.00%</td>\n",
       "      <td id=\"T_5de79_row17_col8\" class=\"data row17 col8\" >-9.86%</td>\n",
       "      <td id=\"T_5de79_row17_col9\" class=\"data row17 col9\" >4.48%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row18\" class=\"row_heading level0 row18\" >155.57</th>\n",
       "      <td id=\"T_5de79_row18_col0\" class=\"data row18 col0\" >-6.91%</td>\n",
       "      <td id=\"T_5de79_row18_col1\" class=\"data row18 col1\" >-2.13%</td>\n",
       "      <td id=\"T_5de79_row18_col2\" class=\"data row18 col2\" >-2.39%</td>\n",
       "      <td id=\"T_5de79_row18_col3\" class=\"data row18 col3\" >-0.39%</td>\n",
       "      <td id=\"T_5de79_row18_col4\" class=\"data row18 col4\" >13.75%</td>\n",
       "      <td id=\"T_5de79_row18_col5\" class=\"data row18 col5\" >2.03%</td>\n",
       "      <td id=\"T_5de79_row18_col6\" class=\"data row18 col6\" >3.18%</td>\n",
       "      <td id=\"T_5de79_row18_col7\" class=\"data row18 col7\" >11.79%</td>\n",
       "      <td id=\"T_5de79_row18_col8\" class=\"data row18 col8\" >-5.69%</td>\n",
       "      <td id=\"T_5de79_row18_col9\" class=\"data row18 col9\" >18.81%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row19\" class=\"row_heading level0 row19\" >217.8</th>\n",
       "      <td id=\"T_5de79_row19_col0\" class=\"data row19 col0\" >-6.14%</td>\n",
       "      <td id=\"T_5de79_row19_col1\" class=\"data row19 col1\" >-3.64%</td>\n",
       "      <td id=\"T_5de79_row19_col2\" class=\"data row19 col2\" >0.02%</td>\n",
       "      <td id=\"T_5de79_row19_col3\" class=\"data row19 col3\" >0.19%</td>\n",
       "      <td id=\"T_5de79_row19_col4\" class=\"data row19 col4\" >1.63%</td>\n",
       "      <td id=\"T_5de79_row19_col5\" class=\"data row19 col5\" >8.81%</td>\n",
       "      <td id=\"T_5de79_row19_col6\" class=\"data row19 col6\" >1.85%</td>\n",
       "      <td id=\"T_5de79_row19_col7\" class=\"data row19 col7\" >6.87%</td>\n",
       "      <td id=\"T_5de79_row19_col8\" class=\"data row19 col8\" >-9.38%</td>\n",
       "      <td id=\"T_5de79_row19_col9\" class=\"data row19 col9\" >-4.28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row20\" class=\"row_heading level0 row20\" >304.91</th>\n",
       "      <td id=\"T_5de79_row20_col0\" class=\"data row20 col0\" >3.91%</td>\n",
       "      <td id=\"T_5de79_row20_col1\" class=\"data row20 col1\" >-2.88%</td>\n",
       "      <td id=\"T_5de79_row20_col2\" class=\"data row20 col2\" >-1.97%</td>\n",
       "      <td id=\"T_5de79_row20_col3\" class=\"data row20 col3\" >-3.06%</td>\n",
       "      <td id=\"T_5de79_row20_col4\" class=\"data row20 col4\" >3.99%</td>\n",
       "      <td id=\"T_5de79_row20_col5\" class=\"data row20 col5\" >6.91%</td>\n",
       "      <td id=\"T_5de79_row20_col6\" class=\"data row20 col6\" >5.36%</td>\n",
       "      <td id=\"T_5de79_row20_col7\" class=\"data row20 col7\" >63.53%</td>\n",
       "      <td id=\"T_5de79_row20_col8\" class=\"data row20 col8\" ></td>\n",
       "      <td id=\"T_5de79_row20_col9\" class=\"data row20 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row21\" class=\"row_heading level0 row21\" >426.88</th>\n",
       "      <td id=\"T_5de79_row21_col0\" class=\"data row21 col0\" >-4.17%</td>\n",
       "      <td id=\"T_5de79_row21_col1\" class=\"data row21 col1\" >6.46%</td>\n",
       "      <td id=\"T_5de79_row21_col2\" class=\"data row21 col2\" >0.61%</td>\n",
       "      <td id=\"T_5de79_row21_col3\" class=\"data row21 col3\" >6.72%</td>\n",
       "      <td id=\"T_5de79_row21_col4\" class=\"data row21 col4\" ></td>\n",
       "      <td id=\"T_5de79_row21_col5\" class=\"data row21 col5\" >2.56%</td>\n",
       "      <td id=\"T_5de79_row21_col6\" class=\"data row21 col6\" >-4.56%</td>\n",
       "      <td id=\"T_5de79_row21_col7\" class=\"data row21 col7\" >97.43%</td>\n",
       "      <td id=\"T_5de79_row21_col8\" class=\"data row21 col8\" ></td>\n",
       "      <td id=\"T_5de79_row21_col9\" class=\"data row21 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row22\" class=\"row_heading level0 row22\" >597.63</th>\n",
       "      <td id=\"T_5de79_row22_col0\" class=\"data row22 col0\" ></td>\n",
       "      <td id=\"T_5de79_row22_col1\" class=\"data row22 col1\" >19.41%</td>\n",
       "      <td id=\"T_5de79_row22_col2\" class=\"data row22 col2\" >3.26%</td>\n",
       "      <td id=\"T_5de79_row22_col3\" class=\"data row22 col3\" >7.32%</td>\n",
       "      <td id=\"T_5de79_row22_col4\" class=\"data row22 col4\" ></td>\n",
       "      <td id=\"T_5de79_row22_col5\" class=\"data row22 col5\" >-3.25%</td>\n",
       "      <td id=\"T_5de79_row22_col6\" class=\"data row22 col6\" ></td>\n",
       "      <td id=\"T_5de79_row22_col7\" class=\"data row22 col7\" ></td>\n",
       "      <td id=\"T_5de79_row22_col8\" class=\"data row22 col8\" ></td>\n",
       "      <td id=\"T_5de79_row22_col9\" class=\"data row22 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de79_level0_row23\" class=\"row_heading level0 row23\" >836.68</th>\n",
       "      <td id=\"T_5de79_row23_col0\" class=\"data row23 col0\" >-0.48%</td>\n",
       "      <td id=\"T_5de79_row23_col1\" class=\"data row23 col1\" ></td>\n",
       "      <td id=\"T_5de79_row23_col2\" class=\"data row23 col2\" >-0.16%</td>\n",
       "      <td id=\"T_5de79_row23_col3\" class=\"data row23 col3\" ></td>\n",
       "      <td id=\"T_5de79_row23_col4\" class=\"data row23 col4\" ></td>\n",
       "      <td id=\"T_5de79_row23_col5\" class=\"data row23 col5\" ></td>\n",
       "      <td id=\"T_5de79_row23_col6\" class=\"data row23 col6\" ></td>\n",
       "      <td id=\"T_5de79_row23_col7\" class=\"data row23 col7\" ></td>\n",
       "      <td id=\"T_5de79_row23_col8\" class=\"data row23 col8\" ></td>\n",
       "      <td id=\"T_5de79_row23_col9\" class=\"data row23 col9\" ></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2c0d550a0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_W_Metric_raw = dataset[['difficulty', 'stability', 'p', 'y']].copy()\n",
    "B_W_Metric_raw['s_bin'] = B_W_Metric_raw['stability'].map(lambda x: round(math.pow(1.4, math.floor(math.log(x, 1.4))), 2))\n",
    "B_W_Metric_raw['d_bin'] = B_W_Metric_raw['difficulty'].map(lambda x: int(round(x)))\n",
    "B_W_Metric = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('mean').reset_index()\n",
    "B_W_Metric['B-W'] = B_W_Metric['p'] - B_W_Metric['y']\n",
    "B_W_Metric_pivot = B_W_Metric.pivot(index=\"s_bin\", columns='d_bin', values='B-W')\n",
    "B_W_Metric_pivot.apply(pd.to_numeric).style.background_gradient(cmap='seismic', axis=None, vmin=-0.2, vmax=0.2).format(\"{:.2%}\", na_rep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='d_bin'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "B_W_Metric_raw.groupby(by=['d_bin']).agg('count').reset_index().plot.bar(x='d_bin', y='p', legend=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fsrs4anki",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
